<p dir="ltr" id="isPasted">In the latest BrainChip podcast hear from Zach about Edge Impulses astounding metrics for developer recruitment, training volume, and adoption; using AI to fill the gap when data sets are small or non-existent; the challenges of bringing AI to manufacturing and real-world use cases.</p><p dir="ltr"><a href="https://www.youtube.com/watch?v=f2hD-kHssg0" target="_blank">Listen to the full episode here.</a> Some of the highlights from the conversation, worth tuning in for include:</p><h2 dir="ltr">Reducing time and effort with automated data labeling and processing</h2><p dir="ltr">One of the pivotal advancements in AI and Edge Computing is the automation of data labeling and processing. The use of big segmenter models for auto-labeling imagery and other data types has been a game-changer, significantly reducing the time and effort required in data preparation. This automation is not just a matter of convenience but a critical step in enhancing the efficiency and accuracy of AI models. For engineers, this means an accelerated process from concept to deployment, enabling a more agile response to industry needs.</p><p dir="ltr">The implications of automated data labeling extend beyond mere efficiency. It represents a shift in how engineers approach data - from a task-intensive process to a more streamlined, model-driven approach. This shift is crucial in industries where the volume of data is vast and the need for precision is paramount. The ability to quickly identify and utilize relevant data points without extensive manual intervention is a substantial leap forward in AI and Edge Computing.</p><h3 dir="ltr">The challenge of LLMs and the power of the developer ecosystem<br><br></h3><p dir="ltr">Despite the advancements, the deployment of AI models, especially Large Language Models (LLMs), in edge computing environments poses significant challenges. The reliability of these models in critical scenarios, such as industrial maintenance or medical diagnostics, is a primary concern. Engineers are tasked with ensuring that these models provide accurate and reliable outputs every time, a non-negotiable requirement in high-stakes environments.</p><p dir="ltr">The podcast highlights the growth in the developer ecosystem, particularly in platforms like Edge Impulse, which has seen its community double in size within a year. This growth is indicative of the increasing interest and adoption of AI and edge computing technologies across various sectors. Real-world deployments in healthcare, logistics, and manufacturing are testament to the practical applications of these technologies. From medical transcription to predictive maintenance in manufacturing, the scope of AI and Edge Computing is vast and continually expanding.<br><br></p><p dir="ltr">However, the journey from training to deployment is fraught with complexities. Engineers must navigate the challenges of diverse hardware environments and the unique demands of edge computing scenarios. This requires a deep understanding of both the technological and practical aspects of AI model deployment, ensuring that the solutions are not only technologically sound but also viable in real-world settings.<br><br></p><h3 dir="ltr">Beyond the hype: Making AI practical and reliable</h3><p>The industry anticipates a shift towards more practical and reliable AI solutions. The focus is expected to move away from pursuing exotic innovations to establishing clear guidelines and stable hardware for successful AI and edge computing applications. For engineers, this means a greater emphasis on developing solutions that are not only advanced but also robust and easily deployable in various environments.<br><br></p><p dir="ltr">Collaborations, like the one between Edge Impulse and Nvidia, are pivotal in this context. They bring foundational AI models to the edge, optimizing them for diverse hardware requirements. Such partnerships are crucial in bridging the gap between cutting-edge AI research and practical, deployable solutions.<br><br></p><p dir="ltr">As the technology evolves, the focus for engineers will increasingly be on creating solutions that are not just innovative but also reliable, efficient, and tailored to specific industry needs. The future of AI and Edge Computing is poised to be driven by practicality, reliability, and a deep understanding of the real-world contexts in which these technologies will operate.</p><p><a href="https://www.wevolver.com/profile/brainchip" target="_blank">Read more about BrainChip here.</a></p>

In the latest BrainChip podcast episode, host Nandan Nayampally sat down with Zach Shelby, CEO of Edge Impulse, to talk about AI at the Edge. 


BrainChip Podcast:  AI after the Hype Cycle

<p><em>This article is a summary of the full white paper "Finding Bacteria in Blood: Scaling A Hardware-Driven Neuromorphic Solution for Real-World E-Nose Applications".&nbsp;</em><a href="https://brainchip.com/finding-bacteria-in-blood-a-hardware-based-neuromorphic-solution-for-real-world-e-nose-applications/" target="_blank" rel="noopener noreferrer"><em>Read it here.</em></a></p><h2>Neuromorphic approach to replicating human senses</h2><p dir="ltr" id="isPasted">Technology has made significant strides in replicating human senses and mimicking the sense of smell. Electronic noses (e-noses), devices designed to detect and analyze odors, have become integral in medical diagnostics and food quality control. However, current e-nose technologies face portability, energy efficiency, and accuracy limitations. Recent research presents an innovative neuromorphic approach, using spiking neural networks and data-to-spike encoding, to overcome these challenges, offering a more effective and practical e-nose solution.</p><h2 dir="ltr">The Experiment: Detecting Bacteria in Blood</h2><h3 dir="ltr">Dataset Overview</h3><p dir="ltr">The 'bacteria in blood' dataset, integral to this research, was collected as part of the Mednose project at Örebro University. This dataset is pivotal for the early detection of various bacterial species in blood samples, serving as a critical tool for rapid and accurate diagnostics. It encompasses a comprehensive collection of 1200 samples, with each of the ten bacterial species represented by 120 samples. This extensive dataset is instrumental in understanding the unique chemical signatures emitted by different bacteria, which is crucial for effective diagnosis and treatment strategies.</p><h3 dir="ltr">Data Preprocessing and Encoding</h3><p dir="ltr">The preprocessing of this dataset involves baseline cancellation to eliminate background noise and scaling of sensor responses for consistency. Two innovative encoding algorithms, Address Event Representation for Olfaction (AERO) and Step Forward (SF), were employed to convert sensor data into a digital format. AERO structures sensor information into a grid, creating digital snapshots of scents. SF focuses on changes in odor data exceeding a certain threshold, inspired by the human sensory system's attention to dynamic information. These algorithms effectively capture and interpret the complex odor signatures associated with different bacteria types.</p><p dir="ltr"><a href="https://brainchip.com/akida-generations/" target="_blank" rel="noopener noreferrer">The Akida™ classifier model</a> developed in this study demonstrated a high classification accuracy of 97.42% in identifying ten different bacteria species, marking a significant advancement in electronic noses and neuromorphic computing.</p><h2 dir="ltr">Achieving High Accuracy and Efficiency</h2><p dir="ltr">In neuromorphic computing, enhancing electronic noses (e-noses) for practical applications such as detecting bacteria in the blood and optimizing the Spiking Neural Network (SNN) is crucial. This optimization is essential for effective odor detection, impacting the system's accuracy, efficiency, and speed.</p><h3 dir="ltr">Optimization Strategies</h3><p dir="ltr">The optimization of the SNN in this context involves fine-tuning various parameters and structures to maximize the efficiency of odor detection. Key aspects include adjusting the number of neurons associated with each odor class, the active connections each neuron should have, and the intensity of learning competition among neurons. This process ensures that the SNN can accurately identify patterns in the incoming data and respond rapidly, effectively mimicking the human brain's neuronal activity.&nbsp;</p><p dir="ltr">These optimizations are rooted in biological processes like Spike Time Dependent Plasticity (STDP), which allows the network to strengthen synaptic connections based on frequently encountered activation patterns. This learning mode is fundamental to how the brain adapts and responds to sensory input, making it an ideal model for neuromorphic systems like the Akida NSoC.</p><h3 dir="ltr">Performance Evaluation</h3><p dir="ltr">The performance of the optimized SNN is evaluated based on several critical factors: classification accuracy, power efficiency, and speed of odor detection. In the case of detecting bacteria in blood, the system demonstrated high mean classification accuracies, especially with the early data points in the sampling process. This indicates that the essential features for odor differentiation are present early on, allowing for rapid and accurate classification.</p><p dir="ltr">Power efficiency is another crucial metric directly impacting the system's viability in practical applications. The optimized SNN, as implemented in the Akida NSoC, shows remarkable energy efficiency, consuming minimal power while maintaining high throughput. This aspect is essential for portable or remote applications where power resources are limited.</p><p dir="ltr">The speed of odor detection is the final critical measure of the system's performance. The optimized SNN demonstrates the ability to process and classify odors swiftly, a vital feature for applications requiring real-time analysis, such as medical diagnostics or food quality control.&nbsp;</p><p dir="ltr">Optimizing the SNN for e-nose applications, as demonstrated in the Akida NSoC, represents a significant advancement in neuromorphic technology. It combines high accuracy, energy efficiency, and speed. It is a promising solution for real-world applications in healthcare diagnostics and other sectors where rapid and reliable odor detection is crucial.</p><h2 dir="ltr">Real-World Applications and Implications</h2><p dir="ltr">As demonstrated in the study "<a href="https://brainchip.com/finding-bacteria-in-blood-a-hardware-based-neuromorphic-solution-for-real-world-e-nose-applications/" target="_blank">Finding Bacteria in Blood</a>," neuromorphic technology shows immense potential in transforming healthcare and rapid disease diagnosis. This technology can significantly speed up the identification of various strains of bacteria in the blood, which is crucial for diagnosing diseases accurately and promptly. Its portability and accuracy make it an invaluable tool in remote locations or outside traditional clinical facilities, providing critical healthcare services in underserved areas.</p><p dir="ltr">Beyond healthcare, neuromorphic technology has broader applications in agriculture, environmental monitoring, and industrial processes. Its ability to mimic human sensory systems allows for advanced detection and analysis capabilities, making it a valuable tool for quality control in food production, pollutant detection, and much more.</p><h2 dir="ltr">Conclusion</h2><p dir="ltr">The advancements in neuromorphic technology, in sensory applications like electronic noses, herald a new era in AI and machine learning. Its transformative potential lies in bringing more efficient, accurate, and low-power solutions to real-world challenges. As this technology continues to evolve, it promises to revolutionize numerous industries, improving processes and outcomes across multiple sectors.</p><h2 dir="ltr">Additional Information</h2><p dir="ltr">Please refer to the white paper "<a href="https://brainchip.com/wp-content/uploads/2023/11/Bacteria-in-Blood-research-paper_Final.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">Finding Bacteria in Blood: Scaling A Hardware-Driven Neuromorphic Solution for Real-World E-Nose Applications</a>" for further reading and detailed insights. It provides an in-depth exploration of the technology and its applications. Additionally, for a broader understanding of neuromorphic applications in medicine, the paper "<a href="https://pubmed.ncbi.nlm.nih.gov/37531951/#:~:text=Neuromorphic%20engineering%2C%20which%20uses%20neural,footprint%2C%20and%20high%20bandwidth%20solutions." target="_blank">Neuromorphic applications in Medicine</a>," published on PubMed, offers a comprehensive overview.</p><h2 dir="ltr">References</h2><ol><li dir="ltr"><p dir="ltr"><a href="https://brainchip.com/wp-content/uploads/2023/11/Bacteria-in-Blood-research-paper_Final.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">https://brainchip.com/wp-content/uploads/2023/11/Bacteria-in-Blood-research-paper_Final.pdf</a></p></li><li dir="ltr"><p dir="ltr"><a href="https://brainchip.com/finding-bacteria-in-blood-a-hardware-based-neuromorphic-solution-for-real-world-e-nose-applications/" target="_blank">https://brainchip.com/finding-bacteria-in-blood-a-hardware-based-neuromorphic-solution-for-real-world-e-nose-applications/</a></p></li><li dir="ltr"><p dir="ltr"><a href="https://pubmed.ncbi.nlm.nih.gov/37531951/#:~:text=Neuromorphic%20engineering%2C%20which%20uses%20neural,footprint%2C%20and%20high%20bandwidth%20solutions" target="_blank">https://pubmed.ncbi.nlm.nih.gov/37531951/#:~:text=Neuromorphic%20engineering%2C%20which%20uses%20neural,footprint%2C%20and%20high%20bandwidth%20solutions</a>.</p></li><li dir="ltr"><p dir="ltr"><a href="https://brainchip.com/akida-neural-processor-soc/" target="_blank">https://brainchip.com/akida-neural-processor-soc/</a></p></li><li dir="ltr"><p dir="ltr"><a href="https://brainchip.com/akida-neural-processor-ip/" target="_blank">https://brainchip.com/akida-neural-processor-ip/</a></p></li></ol>

Exploring the Fusion of Neuromorphic Computing and Electronic Noses for Advanced Sensory Solutions


Neuromorphic E-Noses in Medical and Industrial Applications

<h1 dir="ltr" id="isPasted">Introduction</h1><p dir="ltr">In an age where data is the new gold, and digital infrastructure forms the backbone of our society, the importance of data security cannot be overstated. Silicon-level security refers to the protective measures embedded at the hardware level of computing devices. It is a fundamental layer that underpins the reliability and trustworthiness of everything from consumer electronics to critical national infrastructure.</p><p dir="ltr">As we usher in the era of ubiquitous computing, with billions of connected devices exchanging data every second, the stakes for robust security have never been higher. The concept of silicon-level security isn't new; however, the complexity and sophistication of threats it must mitigate have evolved dramatically. Gone are the days when simple perimeter defenses were sufficient. Today's security architects must assume a posture of 'defense in depth', where multiple layers of security measures are integrated into the silicon itself.</p><p dir="ltr">This proactive approach to security is vital as the functionality and value of devices increasingly hinge on their ability to protect and process sensitive data securely. Whether it's financial transactions, personal information, or state secrets, the common denominator is the silicon that processes and stores this data. Thus, the integrity of silicon-level security mechanisms is a paramount concern, laying the foundation for trust in the digital age.</p><p dir="ltr">In the following sections, we'll delve into the intricate world of secure interface technologies, unpack the challenges faced by modern System on Chip (SoC) designs, and explore how advancements in technology are paving the way for stronger, more resilient security measures. &nbsp;&nbsp;</p><h1 dir="ltr">The Backbone of Security: Root of Trust and Cryptography IP</h1><p dir="ltr">At the heart of silicon-level security is the concept of a 'Root of Trust' (RoT), a set of functions trusted implicitly by the system due to their foundational role in ensuring overall security. The RoT provides a secure starting point for system boot and cryptographic operations and is immune to software-based attacks because it is often implemented in hardware. This secure cornerstone handles critical tasks such as secure boot, secure firmware updates, and cryptographic key management, ensuring that only authenticated code and communications are running on the device.</p><p dir="ltr">Adjacent to the Root of Trust is the role of Cryptography IP, which includes hardware-based cryptographic engines designed to perform encryption and decryption tasks. These cryptographic modules serve as the workhorses for data protection, enabling secure data storage and transmission, and are essential for functions like secure communications, digital signatures, and user authentication.</p><p dir="ltr">Both RoT and Cryptography IP are imperative for a secure boot process, which is the first step in a secure chain of events that occur when a device is powered on. During this process, the integrity and authenticity of the software stack are verified using cryptographic checks, establishing a secure execution environment from the outset.</p><p dir="ltr">To understand their importance, consider the analogy of a bank vault. In this analogy, RoT is the combination to the vault—highly confidential and used to validate the identity of individuals attempting to access the vault. Cryptography IP, on the other hand, is like the reinforced walls and time-locked doors—structural features that enforce security protocols and prevent unauthorized access.</p><p dir="ltr">These technologies also enable secure end-to-end communication, ensuring that data remains confidential and unaltered during transmission. With the proliferation of the Internet of Things (IoT) and connected devices, securing data in transit has become as crucial as securing it at rest.</p><p dir="ltr">Despite their robustness, the implementation of Root of Trust and Cryptography IP is not without challenges. Ensuring that these security measures themselves are impervious to exploitation requires meticulous design and regular updates in response to the evolving threat landscape. In the next section, we will examine the security challenges inherent in modern SoC designs and discuss how these foundational technologies can help mitigate those risks.</p><h1 dir="ltr">Securing the Core: Challenges in SoC Designs</h1><p dir="ltr">Modern Systems on Chip (SoCs) are marvels of integration, embedding processors, memory, I/O, and various other functionalities into a single chip. This consolidation brings vast benefits in terms of performance and power efficiency but also introduces a myriad of security challenges that must be navigated carefully.</p><p dir="ltr">One of the primary challenges is the increased attack surface. The very features that make SoCs powerful—connectivity, configurability, and smart functionalities—also make them more vulnerable to attacks. With more lines of code, there are more potential bugs or backdoors. With more interfaces, there are more points of entry for malicious actors.</p><p dir="ltr">Additionally, as SoCs become more complex, ensuring that every component is secure from design to decommission becomes harder. This complexity often results in a greater risk of side-channel attacks, where an attacker could infer sensitive information from power consumption, electromagnetic emissions, or even computation time.</p><p dir="ltr">Supply chain security presents another significant challenge. SoCs are global products, with materials and components sourced from and passing through multiple vendors and countries. Each step in this chain presents an opportunity for malicious actors to introduce vulnerabilities.</p><p dir="ltr">Moreover, the need for speed in markets means that security can sometimes be an afterthought, leading to vulnerabilities in the rush to get products to market. As a result, rigorous security testing and validation procedures are essential but can be overlooked or undervalued.</p><p dir="ltr">In dealing with these challenges, the industry has turned to a variety of mitigation strategies. One key approach is the concept of 'security by design', where security measures are considered at every step of the SoC design and manufacturing process. This approach includes using hardware-based security features, like those provided by secure enclaves and trusted execution environments, which can isolate sensitive operations from the main processor to protect them from software attacks.</p><p dir="ltr">Another strategy is regular security audits and updates. Even after an SoC is deployed, its security posture must be maintained and updated in response to new threats—much like how software is regularly patched.</p><p dir="ltr">Finally, leveraging advanced fabrication techniques and materials can reduce the physical vulnerabilities of SoCs, making them harder to tamper with or reverse engineer.</p><p dir="ltr">Navigating these challenges is a complex task, but it is one that is crucial for maintaining trust in technology. In the next section, we discuss how these strategies are put into practice, keeping SoCs secure against an ever-evolving array of threats.</p><h1 dir="ltr">Building Defenses: Mitigation Strategies for SoC Security</h1><p dir="ltr">To confront the security challenges in SoC designs, the semiconductor industry has developed a suite of mitigation strategies that bolster the defenses of these complex systems.</p><ul><li dir="ltr"><p dir="ltr"><strong>Secure Architectural Design:</strong> The first line of defense is secure architectural design. This includes the incorporation of hardware-based security features such as secure boot, trusted execution environments, and secure key storage. By integrating these elements at the design phase, SoCs are better equipped to handle unauthorized access attempts and ensure that critical operations remain uncompromised.</p></li><li dir="ltr"><p dir="ltr"><strong>Layered Security Approach:</strong> A layered security approach is critical for protecting against a wide range of potential attacks. This involves implementing multiple security layers that function together to protect the system. If one layer is breached, the subsequent layers provide an additional level of defense, making it more difficult for an attack to be successful.</p></li><li dir="ltr"><p dir="ltr"><strong>Dynamic Security Measures:&nbsp;</strong>Dynamic security measures, such as runtime monitoring and anomaly detection, are also key. These systems can detect and respond to suspicious behavior in real-time, providing a responsive layer of security that adapts to current threats.</p></li><li dir="ltr"><p dir="ltr"><strong>Encryption and Cryptography:&nbsp;</strong>Strong encryption and cryptography are indispensable in SoC security, safeguarding data both at rest and in transit. Advanced encryption standards and cryptographic algorithms are implemented to ensure that even if data is intercepted, it remains indecipherable without the appropriate keys.</p></li><li dir="ltr"><p dir="ltr"><strong>Regular Security Audits and Firmware Updates:&nbsp;</strong>Ongoing security audits and regular firmware updates ensure that security measures remain up-to-date in the face of evolving threats. This strategy includes the establishment of secure update mechanisms that prevent the installation of malicious firmware.</p></li><li dir="ltr"><p dir="ltr"><strong>Supply Chain Integrity:&nbsp;</strong>Protecting the integrity of the supply chain is another critical strategy. This involves measures such as secure manufacturing practices, tamper-evident packaging, and traceability of components to ensure that SoCs are not compromised at any point from fabrication to delivery.</p></li><li dir="ltr"><p dir="ltr"><strong>User Education and Access Control:</strong> Lastly, user education and strict access control policies are necessary to prevent inadvertent breaches. Users need to be aware of the security features of their devices and how to use them properly, while access control ensures that only authorized individuals can make changes to the system configuration or access sensitive information.</p></li></ul><p dir="ltr">By implementing these strategies, SoC designers and manufacturers can create more secure systems that are resilient in the face of both current and emerging security threats. However, the fast pace of technological change means that vigilance and continuous improvement are mandatory. Security is not a one-time achievement but an ongoing process of adaptation and reinforcement.</p><h1 dir="ltr">Conclusion</h1><p dir="ltr">Silicon-level security stands as a critical pillar in our digital era. Through exploring secure interface technologies, such as Root of Trust and Cryptography IP, we recognize their indispensable role in safeguarding modern SoCs. As we confront security challenges—from side-channel attacks to complex interface vulnerabilities—the need for a robust, multi-layered defense strategy becomes clear.</p><p dir="ltr">Securing silicon goes beyond a single solution—it's a continuous battle, balancing innovation with vigilance. As technology advances, so must our security strategies, integrating state-of-the-art features and adopting a culture of security-first in design and deployment.</p><p dir="ltr">The future hinges on our collective effort to ensure the trustworthiness of our digital foundations. In fostering this trust, the commitment to silicon-level security must remain unwavering, uniting stakeholders across the industry to fortify our digital landscape.</p><h1 dir="ltr">Synopsys' Impact on SoC Security</h1><p dir="ltr">In the realm of SoC security, <a href="https://www.synopsys.com/" target="_blank" rel="noopener noreferrer">Synopsys</a> offers critical technologies that sharpen the defenses of silicon interfaces. Their AI and Machine Learning tools not only expedite chip design but also imbue systems with smart threat detection, essential for anticipatory security. Their cloud solutions provide secure integrations essential for IoT devices dependent on cloud data management.</p><p dir="ltr">Their Design Technology Co-Optimization (DTCO) propels the creation of SoCs resistant to advanced threats by exploring new transistor architectures. Synopsys' emphasis on early-stage security integration through DevSecOps, alongside energy-efficient design practices, further strengthens SoCs against exploits like power analysis attacks.</p><p dir="ltr">Synopsys also pioneers in fortifying system integrity with its Multi-Die System solutions, safeguarding sensitive operations within SoCs. Their vigilance extends to software security, ensuring open-source components and software supply chains are robust against vulnerabilities from inception.</p><p dir="ltr">Through innovation and a strategic approach to security, Synopsys plays a pivotal role in the evolution of more secure digital infrastructures, demonstrating a comprehensive commitment to the advancement of SoC security.</p><h1 dir="ltr" id="isPasted">References</h1><p dir="ltr">[1] Dana Neustadter, Hezi Saar, Michael Posner, ‘How Security for SoC Interfaces Enhances Data Protection’, Synopsis, 4 Oct. 2022, [Online], Available from:&nbsp;<a href="https://www.synopsys.com/blogs/chip-design/soc-interfaces-security-enhance-data-protection.html" target="_blank">https://www.synopsys.com/blogs/chip-design/soc-interfaces-security-enhance-data-protection.html</a></p><p dir="ltr">[2] Marco Ciaffi, John Min, ‘Building security into an AI SoC using CPU features with extensions’, Embedded, 12 Apr. 2021, [Online], Available from:&nbsp;<a href="https://www.embedded.com/building-security-into-an-ai-soc-using-cpu-features-with-extensions/" target="_blank">https://www.embedded.com/building-security-into-an-ai-soc-using-cpu-features-with-extensions/</a></p><p dir="ltr">[3] Ruud Derwig, Nicole Fern, ‘Techniques for Ensuring Security in Processor-based SoCs’, Synopsis, [Online], Available from: <a href="https://www.synopsys.com/designware-ip/technical-bulletin/ensuring-security-processor-socs.html" target="_blank">https://www.synopsys.com/designware-ip/technical-bulletin/ensuring-security-processor-socs.html</a></p><p dir="ltr">[4] Landry J., ‘What Is Hardware Root of Trust?’, Dell, 22 July 2022, [Online], Available from: <a href="https://www.dell.com/en-us/blog/hardware-root-trust/" target="_blank" rel="noopener noreferrer">https://www.dell.com/en-us/blog/hardware-root-trust/</a></p>

Silicon-level security is crucial in protecting the hardware of computing devices, serving as a foundational layer for data integrity and system reliability in our increasingly digital world.


Silicon-Level Security: A Deep Dive into Secure Interfaces

<p dir="ltr" id="isPasted">Moore’s Law and distributed cloud computing have enabled artificial intelligence (AI) and machine learning (ML) applications to effectively overcome Von Neumann bottlenecks that once limited data throughput on conventional systems. With enormous amounts of targeted compute power and memory available in the cloud, AI/ML training and inference models continue to increase in both size and sophistication.</p><p dir="ltr">However, cloud-based data centers have created a new set of challenges for AI applications at the edge such as latency, power, and security. Untethering edge AI from the cloud helps address some of these issues—and creates opportunities for the semiconductor industry to design new products with smarter and more independent sensors, devices, and systems.</p><p dir="ltr">For example, autonomous vehicles leverage cloud-free, edge AI learning at high speeds to continuously update and define safety parameters that make it easier for onboard systems to detect anomalous structural vibrations and engine sounds.</p><p dir="ltr">Cloud-free edge AI also enables gesture control with faster response times, allowing doctors and therapists to help people with disabilities interact with sophisticated robotic assistance devices. In the future, field hospitals in disaster zones can deploy medical robots with advanced edge AI capabilities, even if connectivity is limited.</p><p dir="ltr">Lastly, smart farms in remote areas depend on cloud-free AI to help lower food prices by efficiently increasing crop yields with intelligent soil sensors, irrigation systems, and autonomous drones.</p><h2 dir="ltr">The human brain shows us the way</h2><p dir="ltr">Untethering edge AI from the cloud is an important first step to enabling smarter endpoints. However, the semiconductor industry must also acknowledge the inefficiencies of simply scaling down AI hardware at the edge. Indeed, advanced edge applications are already hitting the limits of conventional AI silicon and standard learning models.</p><p dir="ltr">Many chips used in edge applications today are still general-purpose processors such as GPUs that consume approximately 1,000X more power than purpose-built edge silicon. Although some smart devices are equipped with low-power digital signal processors (DSPs), these single-purpose chips typically lack advanced learning and analytics capabilities.</p><p dir="ltr">In recent years, neuromorphic computing has unlocked more efficient architectures and learning models such as incremental and one-shot learning for edge AI. Smart edge silicon that follows the principles of essential AI—doing more with less—now supports a new generation of advanced multimodal and autonomous use cases with independent learning and inference capabilities, faster response times, and a lower power budget.</p><p dir="ltr">These include self-driving cars that personalize cabin settings for individual drivers, automated factories and warehouses, advanced speech and facial recognition applications, and robots that use sophisticated sensors to see, hear, smell, touch, and taste.&nbsp;</p><h2 dir="ltr">The future’s not only bright, it’s essential</h2><p dir="ltr">Edge AI presents both a challenge and opportunity for the semiconductor industry to look well beyond cutting the cord to the cloud and simply scaling down conventional chip architectures. Neuromorphic edge AI silicon is already enabling people to seamlessly interact with smarter devices that independently learn new skills, intelligently anticipate requests, and instantly deliver services.</p><p dir="ltr">Specific strategies to unlocking the full potential of edge AI will undoubtedly vary, which is why it is important to explore a future in which semiconductor companies play a collaborative role in helping to design and implement new neuromorphic architectures.</p><h2 dir="ltr">Edge AI: The Cloud-Free Future is Now Whitepaper</h2><p dir="ltr">Brainchip and the Global Semiconductor Alliance (GSA) have jointly published a white paper titled “Edge AI: The Cloud-Free Future is Now” that discusses:</p><ul><li dir="ltr"><p dir="ltr">Untethering edge AI from the cloud</p></li><li dir="ltr"><p dir="ltr">Overcoming the limits of conventional AI silicon and learning methodologies</p></li><li dir="ltr"><p dir="ltr">Unlocking new architectures and learning models with neuromorphic computing</p></li><li dir="ltr"><p dir="ltr">Supporting autonomous edge AI applications</p></li></ul><p dir="ltr">“We are excited to support the publication of this white paper as it is a call to our industry to work together to address the unique learning and performance requirements of edge AI,” said Brainchip Marketing and Strategy Advisor, Jerome Nadel. “We hope it provokes conversation and collaboration—and ultimately enables effective edge compute to be universally deployable across real world applications such as connected cars, consumer electronics, and industrial IoT.”</p><p dir="ltr"><a href="https://brainchip.com/#whitepaper" target="_blank" rel="noopener noreferrer" class="button-main-action">Click here to read the full white paper.</a></p><h2 dir="ltr">Building collective knowledge</h2><p dir="ltr">Over the next few months, as part of our new series spotlighting Neuromorphic computing, we will share content that explores how neuromorphic edge silicon can mimic the human brain to analyze only essential sensor inputs at the point of acquisition. Specifically, we’ll take a closer look at the four primary design principles of neuromorphic edge silicon, how to efficiently scale and optimize on-chip memory, as well as strategies for leveraging inference and one-shot learning. We’ll also discuss real-world edge AI use cases, including automotive edge learning at high speeds, wine tasting, object detection and classification, and keyword spotting.</p><p dir="ltr">Follow the <a href="https://www.wevolver.com/profile/brainchip" target="_blank" rel="noopener noreferrer">BrainChip</a> Wevolver profile to stay-up-to date.&nbsp;</p>

This is the first article in a new series from BrainChip highlighting their vision and technology. 


Edge AI: Why the cloud-free future is now

<p dir="ltr" id="isPasted"><em>In a recent episode of the <a href="https://www.youtube.com/@BrainChipInc" target="_blank">BrainChip podcas</a>t, Rob Telson, the Vice President of Sales and Marketing at BrainChip, spoke to Michael Azoff, an industry analyst with a focus on AI at the edge. He has 18 years of experience as an analyst and has previously worked hands-on with neural networks. We highlight some of the key discussion points below but encourage you to listen to the </em><a href="https://www.youtube.com/watch?v=xsIWZXlin7I&ab_channel=BrainChipInc" target="_blank"><em>whole episode here.</em></a></p><h2 dir="ltr">A Dive into the Past and Present</h2><p dir="ltr">During the podcast, Azoff recounted his extensive experience with AI, which began in the 1990s. He reminisced about the early days of neuromorphic computing and how it was just taking root. As the conversation progressed, he highlighted the significant milestones, emphasizing the advancements in training neuromorphic architecture-based neural networks. He also acknowledged BrainChip's notable ascent in recent years, pointing to it as evidence of the growing momentum in the neuromorphic field.</p><h3>Neuromorphic's Niche in Edge AI</h3><p dir="ltr">Azoff's recent contributions to Gigaom's reports have centered on Edge AI, a field that emphasizes inferencing at the edge. In his analysis, the Edge AI domain bifurcates into three distinct categories: ultra-low power, low power, and automotive. Intriguingly, neuromorphic architectures have carved a niche for themselves in the ultra-low power segment. Their inherent efficiency and minimal power consumption make them a natural fit for this category.</p><h3>Applications: From Surveillance to Automotive</h3><p dir="ltr">Neuromorphic architectures excel in scenarios characterized by sparse data. For instance, in visual surveillance, where a static scene is occasionally interrupted by noteworthy events, these architectures prove invaluable. Their design allows them to operate in close proximity to sensors, processing data in real-time. The applications are diverse: smart doorbells, earbuds, industrial fault detection, and even monitoring crowd density at train stations. While vision and audio remain the primary focus areas, the potential applications are vast.</p><p dir="ltr">In the automotive sector, a distinction emerges between in-cabin applications and driving functionalities. While the latter, especially autonomous driving, demands robust processing, neuromorphic computing finds its place in the former. In-cabin applications, ranging from infotainment to human-machine communication, can benefit immensely from neuromorphic's capabilities.</p><h2>Consumer Electronics: The Next Frontier</h2><p dir="ltr">As neuromorphic technology matures, its integration into consumer products is anticipated to grow. The allure of low power consumption cannot be overstated. For application developers, this presents a golden opportunity to innovate, pushing the boundaries of what's possible. In a decade, we might witness a paradigm shift where handheld devices and consumer electronics come equipped with neuromorphic processors, either as accelerators or integral components of the system's ecosystem.</p><h3>AI's Societal Impact and the Dream of Understanding the Human Brain</h3><p dir="ltr">Beyond applications, the podcast touched upon a profound topic: the beneficial impact of AI on society. AI's prowess lies in its ability to automate tasks that surpass human capabilities, especially when scale and complexity come into play. The horizon is vast, with the ultimate goal being the enhancement of AI architectures' intelligence, inching closer to the elusive general artificial intelligence.</p><p>Azoff's vision for an AI superpower is both ambitious and profound: to unravel the mysteries of the human brain. If AI can aid in understanding our brain's intricacies, the implications for neuroscience, psychology, and AI itself could be groundbreaking.</p><h3>In Conclusion</h3><p>This episode of BrainChip's podcast offers a panoramic view of neuromorphic computing's past, present, and future. As we stand on the cusp of AI's next revolution, neuromorphic computing emerges as a beacon, guiding the way forward. The journey is just beginning, and the possibilities are boundless.</p><p><a href="https://www.wevolver.com/profile/brainchip" target="_blank">&nbsp;Stay up to date with BrainChip's new content here.</a></p>

In this episode, Rob Telson speaks with Industry Expert, Michael Azoff, about neuromorphic computing in the AI space including edge AI, neural network architectures, sensory-based applications and more.

BrainChip Podcast: Exploring Neuromorphic Computing with Industry Expert Michael Azoff

<p dir="ltr" id="isPasted"><em>In a recent episode of the <a href="https://www.youtube.com/@BrainChipInc" target="_blank" rel="noopener noreferrer">BrainChip podcas</a>t, a select group of the company's top executives gathered to discuss the future trajectory of artificial intelligence (AI) and the pivotal role BrainChip is set to play in this dynamic landscape. We highlight some of the key discussion points below but encourage you to listen to the&nbsp;</em><a href="https://www.youtube.com/watch?v=zUA9Vdd6cE8&t=2s&ab_channel=BrainChipInc" target="_blank"><em>whole episode here.</em></a></p><h2 dir="ltr">Leadership with Vision</h2><p>Rob Telson Vice President, Ecosystem and Partnerships, is the driving force behind BrainChip's global outreach. With a keen understanding of market dynamics and a vision for BrainChip's expansive global presence, Tolson has been instrumental in positioning the company as a leader in the AI industry.</p><p dir="ltr">Peter Van der Made, <span lang="EN-AU">Executive Director, CTO, and Founder</span> of BrainChip, brings a wealth of experience and a visionary approach to the table. His emphasis on the importance of the company's Advanced Research Center in Perth showcases his commitment to pioneering the next wave of AI innovations. Vandermade's insights into the potential of Akida, BrainChip's flagship technology, highlight his forward-thinking approach to AI's future.</p><p>Anil Mankar, the Vice President of Product Development and also a Co-founder, offers a deep dive into the technical intricacies of BrainChip's operations. His insights into the production process, from chip manufacturing to rigorous testing, provide a glimpse into the meticulous steps BrainChip takes to ensure top-tier functionality and performance.</p><p>Lastly, Ken Scarance, the Chief Financial Officer of BrainChip, sheds light on the company's financial endeavours. His discussions on Brain Chips strategic financial initiatives, including capital raising agreements and efforts to bolster its presence in the U.S. capital markets, underscore the company's ambitions for growth and market dominance.</p><h2 dir="ltr"><strong>BrainChip's Evolution: A Glimpse into the Company's Global Strategy&nbsp;</strong></h2><p>To kick off the episode, the team outlines the company’s history, vision and future plans. BrainChip's commitment to being a global leader in the AI industry is evident in its expansive operational presence. With hubs in California, Perth, France, and India, the company has strategically positioned itself in key tech-centric locations. This global footprint not only facilitates diverse collaborations but also ensures that BrainChip remains at the pulse of AI advancements worldwide.</p><h3 dir="ltr">From Research to Production: A Strategic Pivot</h3><p dir="ltr">Historically, BrainChip has been synonymous with cutting-edge research in neuromorphic computing. Their dedication to pushing the boundaries of AI has positioned them as pioneers in the field. However, recognizing the vast commercial potential of their innovations and the industry's shifting dynamics, BrainChip is undergoing a transformation.</p><p dir="ltr">Tolson emphasised this transition in the podcast, noting the company's pivot from being primarily research-driven to adopting a production-centric approach. For engineers, this shift signifies BrainChip's intent to translate their groundbreaking research into tangible, market-ready solutions.</p><h3 dir="ltr">Engaging the Tech Community</h3><p dir="ltr">Understanding the importance of effective communication in the tech world, especially among engineers and developers, Tolson highlighted BrainChip's efforts to foster engagement. The company's podcast series, for instance, is more than just a marketing tool. It's a platform for knowledge sharing, offering insights into Brain Chips offerings, their vision for AI's future, and the technical intricacies that make their solutions stand out. Stay up to date with BrainChip activities by following their <a href="https://www.wevolver.com/profile/brainchip" target="_blank">Wevolver profile.&nbsp;</a></p><h3 dir="ltr">The Advanced Research Center: A Beacon of Innovation</h3><p dir="ltr">Located in Perth, Australia the Advanced Research Center is BrainChip's crown jewel. It's not just a research facility; it's a testament to the company's dedication to pushing the boundaries of what's possible in AI. While many in the industry focus on refining existing deep learning models, BrainChip's centre is already looking beyond, exploring the next frontier of AI innovations.</p><h3 dir="ltr">Akida: The Future of AI Technology</h3><p dir="ltr">Vandermade's enthusiasm was palpable when discussing <a href="https://brainchip.com/akida-generations/" target="_blank">Akida</a>, BrainChip's flagship technology. Akida is not just another chip in the market; it embodies BrainChip's vision for the future of AI. Two of its standout features are its low energy consumption and its on-chip learning capabilities.</p><p dir="ltr">For engineers and tech enthusiasts, these features are significant. The low energy consumption means that Akida is not only efficient but also environmentally conscious, addressing a growing concern in today's tech-driven world. On the other hand, on-chip learning capabilities represent a leap in AI technology, allowing for faster, more efficient processing without the need for constant back-and-forth with centralised data centres.</p><p dir="ltr">In Vandermade's view, Akida is set to redefine the AI industry. Its unique features position it not just as an innovative product but as a transformative solution that could shape the way we think about and implement AI in various applications.</p><p dir="ltr"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3MjA4NDI5MTg2LWFraWRhLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib"></p><h3 dir="ltr">Financial Initiatives and Market Presence</h3><p dir="ltr">To wrap up the podcast, Ken Scarance, BrainChip's Chief Financial Officer, touched upon the company's recent financial endeavours. He highlighted the company's agreement with LDA, aimed at raising capital, and BrainChip's strategic move to enhance its presence in the U.S. capital markets. Additionally, the company is bolstering its investor relations strategy, aiming to foster better communication with stakeholders and educate the market about BrainChips groundbreaking offerings.</p><h3 dir="ltr">Conclusion</h3><p dir="ltr">As BrainChip continues to make strides in the AI domain, the company remains committed to keeping its audience informed and engaged. With a series of events lined up to showcase Akida's capabilities and an unwavering focus on innovation, BrainChip is undoubtedly poised to redefine the boundaries of AI.<a href="https://www.wevolver.com/profile/brainchip" target="_blank" rel="noopener noreferrer">&nbsp;Stay up to date with BrainChip's new content here.</a></p>

BrainChip's leadership shares details on the company's development of the Akida chip, future prospects, and more in this informative podcast. 

BrainChip Podcast Leadership Roundtable: These are Exciting Times

Article #5 of Spotlight on Innovations in Edge Computing and Machine Learning: A computer vision project that monitors vehicle traffic in real-time using video inferencing performed on the Brainchip Akida Development Kit.

Real-Time Traffic Monitoring with Neuromorphic Computing

<p dir="ltr" id="isPasted">In the dynamic landscape of smart devices, we have witnessed a remarkable evolution over the past decad. Smart devices have transformed from mere gadgets to indispensable companions in our daily lives. From smartphones, which have become extensions of our very selves, to smart home systems that cater to our every need, technology has woven its threads into the fabric of modern society.</p><p dir="ltr">In this ever-evolving market, the demand for seamless and intuitive user experiences with smart technology has grown exponentially. We no longer wish to navigate complex interfaces or juggle multiple devices; we instead seek interactions that mirror the fluidity of human conversation. Enter the era of Voice User Interfaces (VUIs), a transformative innovation that arguably holds the key to the future of smart devices. VUI is a speech-recognition technology that enables us to interact with a device simply through voice commands.</p><p dir="ltr">Imagine this: It’s a sunny morning, and you slowly open your eyes. But instead of reaching for your phone to switch off the alarm, you say, “Good morning, home.” And just like that, your smart home springs into action. The bedroom blinds gracefully draw open, the thermostat adjusts the temperature to your liking, and your espresso machine starts brewing your favorite coffee blend, spreading its aroma through the air.</p><p dir="ltr">From conversing with your smart home that is attuned to your every need to enabling seniors and individuals with disabilities to engage with technology on their own terms, the convenience and accessibility that VUIs offer are incredibly valuable. VUIs transcend the limitations of traditional user interfaces by harnessing the power of edge AI and natural language processing (NLP). They are leading the way in the journey to seamless human-machine interaction.</p><p dir="ltr">In this article, we delve into the evolution of smart devices and how VUIs are changing the way we interact with our devices day to day. We also shed light on the pioneering efforts of Edge Impulse, a cutting-edge platform accelerating innovation in edge AI - particularly in the development of VUIs.</p><p dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="600" src="https://c8056756.sibforms.com/serve/MUIFAMyA6BGksOUPEokHuBnEmZRHhYgJ4RXb4BV0gvzN98ymNIvQdTcBFQnRVfviT6yZSFW_R2g1hENBu7p7Afnyv9Kt0unAAbfFU5mMQhBwI-8hQnKf2r1mmRKhUgDLmtk8bpVSZ7kZGpleCPqtYRv8_kkvODL7F4neGLNGgdpFDm9pPzoYpwbQkc0BU5GVOFoq8CSmD0DDvDjG" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p><h2 dir="ltr">The Journey of Smart Devices: The Rise of VUI</h2><p dir="ltr">Over the past decade, smart devices have become ubiquitous, permeating every aspect of our lives. What began as simple touch-based interactions is evolving into a sophisticated ecosystem encompassing wearables, smart home systems, connected vehicles, and more. The market is witnessing an evolutionary shift as technology is becoming increasingly intertwined with our daily routines, particularly with the considerable development in user interfaces, enabling people to interact with their devices in smoother and more convenient manners.</p><p dir="ltr">From gesture control to acoustic event detection, all the way to enabling <a href="https://www.wevolver.com/article/harnessing-ai-platforms-for-rapid-development-and-deployment-of-predictive-maintenance-systems-in-industrial-applications" target="_blank">predictive maintenance systems</a> in industrial applications, smart systems that leverage human interfaces are growing in popularity and redefining aspects of our daily lives and industry in ways never seen before.</p><p dir="ltr">And the smart device market is poised for even greater expansion. With the major growth in the Internet of Things (IoT) space and the rise of VUI technology, a more seamless interface between humans and machines is coming of age. In fact, the VUI market is growing by leaps and bounds. Valued at just over $13 billion in 2020, the <a href="https://www.alliedmarketresearch.com/voice-user-interface-market-A12381" target="_blank">VUI market</a> is estimated to exceed $95 billion by the end of the decade, recording an impressive compound annual growth rate (CAGR) of 21.5%.</p><h3 dir="ltr">Why are VUIs Becoming Increasingly Prominent?</h3><p dir="ltr">A VUI is an extension of a chatbot-like, <a href="https://www.smashingmagazine.com/2022/02/voice-user-interfaces-guide/" target="_blank">conversation-based interface</a>. The user input it takes in comes in the form of voice. It then processes this input and responds with an output, which can be voice-based or shown on a screen as text or media, accompanied by the respective activity of the smart device. This simple voice activation elevates the <a href="https://edgeimpulse.com/solutions/human-interfaces" target="_blank">human-machine interface</a> beyond touch, resulting in seamless and harmonious communication.</p><p dir="ltr">The simplicity, convenience, and accessibility of VUIs have propelled this technology in recent years, causing it to gather positive momentum in terms of people’s perception of it. Here are some of the notable benefits of VUIs and their application in daily life or even industrial spaces:</p><ol><li dir="ltr"><p dir="ltr">Natural and Intuitive Interaction:&nbsp;VUIs offer a spontaneous and intuitive way of communicating with smart devices, giving the interaction a realistic feel and a perception of a conversation with a virtual assistant.</p></li><li dir="ltr"><p dir="ltr">Hands-Free and Eyes-Free Operation:&nbsp;VUIs go beyond the traditional touch-based interface commonly used in screens. This is extremely valuable when access to screens is difficult, such as during driving, cooking, or other instances where hands and eyes are occupied. This can also be crucial where touch is undesirable, such as in cases where health-related measures are imposed, as was the case during the coronavirus pandemic recently.</p></li><li dir="ltr"><p dir="ltr">Enhanced Accessibility and Inclusivity:&nbsp;One of technology’s core objectives and attributes is accessibility. Enhancing accessibility for everyone and making technology more inclusive is what propels our society forward. VUIs are a game-changer in this aspect. People of old age and people with disabilities, such as visual impairments or limited mobility, may find navigating and controlling devices difficult with traditional user interfaces. Using VUIs, they can interact with their smart devices almost effortlessly by using voice commands.</p></li><li dir="ltr"><p dir="ltr">Higher Convenience and Reliable Delegation:&nbsp;With VUIs, people can control multiple devices in the comfort of their own setting and delegate specific tasks to their smart devices, which enables them to engage in other activities more conveniently and productively, untroubled by anything that the smart device can take care of.</p></li><li dir="ltr"><p dir="ltr">Reduced Learning Curve:&nbsp;Simplicity is crucial for the adoption of technology, especially in our everyday life. VUIs simplify device usage, saving people time and effort that would otherwise be needed to learn complex user interfaces or particular commands. This enables further inclusivity, as it provides a user-friendly interface for individuals of all ages and technological expertise.</p></li></ol><h3 dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="640" height="360" src="https://www.youtube.com/embed/oXThDalYAdY?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span></iframe></span><strong>T</strong>he Baxter Robo<strong>t played a key role in VUI research in industrial robots.&nbsp;</strong></h3><h3 dir="ltr">What is Standing in the Way of VUIs Today?</h3><p dir="ltr">Despite their tremendous growth, VUIs still have some challenges to tackle along the way. The enormous potential of this technology is clear, but it is essential to understand what difficulties are still necessary to overcome both in terms of development and application. Here are some of the challenges of VUIs that we have identified:</p><ol><li dir="ltr"><p dir="ltr">Accuracy in Speech Recognition:&nbsp;Achieving highly accurate speech recognition remains a tough challenge, particularly in dealing with diverse linguistic and regional accents. Currently, speech commands are purposefully minimized to circumvent this issue. However, further development is needed to accommodate a wider range of voice commands, enabling VUIs to record and understand the voice inputs more accurately.</p></li><li dir="ltr"><p dir="ltr">Personalization and Customizability: Tailoring VUIs to individual user preferences is a primary requirement of such technologies, especially with the different priorities, needs, desires, and contexts every user may have. This can be quite challenging, particularly when factoring in the ambiguity that comes with the user's commands and contexts, rendering it hard for the device to deliver relevant and precise responses.</p></li><li dir="ltr"><p dir="ltr">Data Security and Privacy: At the basis of all human interface technologies lies the challenge of security and privacy. Ensuring that the data shared with these smart devices through VUIs is private and secure against any potential unauthorized access or manipulation is paramount, without which people would not have trust and confidence in VUI technology.</p></li></ol><h2 dir="ltr">Edge Impulse Helping Engineers Unleash the Full Potential of VUIs</h2><p dir="ltr">In order to harness the best out of VUIs and tackle the challenges effectively, developers need to consider a holistic approach that involves technological integration, a data-driven mode of work, and human-centered design. This is where Edge Impulse comes into play.</p><p dir="ltr"><a href="https://edgeimpulse.com/" target="_blank" rel="noopener noreferrer">Edge Impulse</a> stands at the forefront of transforming data analysis solutions for businesses and innovators, driven by embedded machine learning and edge computing technologies. With their cutting-edge platform, developers - even without a machine learning background - gain the ability to construct and integrate machine learning models into edge devices almost effortlessly.</p><p dir="ltr">A remarkable aspect of the Edge Impulse platform is its adaptability, allowing for the seamless implementation of VUIs in diverse settings, be it personal or industrial. The Edge Impulse platform simplifies the process of data acquisition and machine learning model development by incorporating various preprocessing methods and a wide range of tools and techniques to train, assess, and optimize ML models. It principally offers developers robust methods to collect real-world sensor, image, and voice data, build reliable ML models, and deploy them at scale.</p><h3 dir="ltr">Case Study: Turning Ordinary Devices into Smart Appliances</h3><p dir="ltr">To enhance the convenience of home appliances, Zalmotek developed a project in which they designed and built a system that integrates basic voice control functionality in any device. They combined a dedicated IoT prototyping platform called Nordic Thingy:53[TM] from Nordic Semiconductors and an audio categorization model customized via the Edge Impulse platform.</p><p dir="ltr">They constructed and trained an ML model for speech recognition using Edge Impulse and capitalized on the multitude of benefits of the platform, particularly:</p><ul><li dir="ltr"><p dir="ltr">The ability to train a high-performing AI model with minimal data</p></li><li dir="ltr"><p dir="ltr">Access to a wide array of tools for optimizing processing power and energy consumption</p></li></ul><p dir="ltr">Such advantages enable users to build models that run efficiently on resource-restricted devices.</p><p dir="ltr">By integrating this ML model with the edge hardware from Nordic, the developers managed to create a solution that simplifies the user interface of any home appliance, unlocking countless possibilities to produce smart appliances with a convenient VUI.</p><p dir="ltr">Explore how Zalmotek designed and built this system by reading the corresponding guide: <a href="https://www.wevolver.com/article/implementing-smart-voice-control-in-appliances-a-comprehensive-guide" target="_blank">Implementing Smart Voice Control in Appliances: A Comprehensive Guide</a></p><h2 dir="ltr">A Peek into the Future of VUIs</h2><p dir="ltr">It is no surprise that VUIs are an exciting technology that can transform how we interact with our smart devices. The integration of VUIs holds immense promise as we can realistically envision a world where our devices anticipate our needs, offer timely and valuable assistance, and seamlessly adapt to our preferences. Market researchers clearly see the value in this space as they estimate substantial growth for the VUI market in the next 5-10 years.</p><p dir="ltr">In this exciting era of VUIs, Edge Impulse emerges as a pioneering platform, fueling the evolution of smart devices through cutting-edge machine learning technology. As the demand for VUIs grows, the Edge Impulse platform will continue to be an invaluable resource for developers, streamlining the creation and deployment of VUIs by enabling and simplifying real-world sensor data collection and accurate machine-learning model development.</p><p dir="ltr">VUIs will no longer be a nice-to-have feature but an integral component of a cohesive and harmonious human-machine interface. Edge Impulse's unwavering commitment to empowering developers to build intelligent and accessible VUIs will undoubtedly propel this space into a more coherent and inclusive future.</p><p dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="600" src="https://c8056756.sibforms.com/serve/MUIFAMyA6BGksOUPEokHuBnEmZRHhYgJ4RXb4BV0gvzN98ymNIvQdTcBFQnRVfviT6yZSFW_R2g1hENBu7p7Afnyv9Kt0unAAbfFU5mMQhBwI-8hQnKf2r1mmRKhUgDLmtk8bpVSZ7kZGpleCPqtYRv8_kkvODL7F4neGLNGgdpFDm9pPzoYpwbQkc0BU5GVOFoq8CSmD0DDvDjG" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p>

Researchers experimenting with VUI for robotic control. Image credit: Azorin, 2013 et al. 

Seamless and intuitive user experiences using speech-recognition technology are enabling us to interact with a device simply through voice commands.

Smart Device Evolution: The Rise of Voice User Interfaces (VUIs)

<p dir="ltr" id="isPasted">In the era of electrification and digitalization, the health of batteries has emerged as a critical factor influencing the performance and longevity of various applications, particularly electric vehicles (EVs) and industrial batteries. Battery health, or the state of health (SoH), is a measure that indicates the level of degradation and remaining capacity of a battery, typically represented as a percentage of its initial capacity.</p><p dir="ltr">As the world increasingly relies on these power sources, the ability to monitor and enhance battery health has become increasingly important. This article aims to explore the application of machine learning in battery health management, focusing on how it can aid in the charging and depletion of electric vehicles and other industrial batteries.<sup id="isPasted">1&nbsp;</sup>We will explore the challenges of implementing such technologies and how Edge Impulse, a leading platform for embedded machine learning, can address these issues.</p><p dir="ltr">The impact of battery health on the operational effectiveness and lifespan of industrial batteries and electric vehicles emphasizes its importance. The number of cycles, temperature, and depth of discharge all impact how slowly batteries' storage capacity declines as they age and go through charge-discharge cycles. <sup id="isPasted">1</sup> This degradation can affect the performance of the devices they power, making battery health a crucial aspect of their maintenance and management.</p><p dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="600" src="https://c8056756.sibforms.com/serve/MUIFAMyA6BGksOUPEokHuBnEmZRHhYgJ4RXb4BV0gvzN98ymNIvQdTcBFQnRVfviT6yZSFW_R2g1hENBu7p7Afnyv9Kt0unAAbfFU5mMQhBwI-8hQnKf2r1mmRKhUgDLmtk8bpVSZ7kZGpleCPqtYRv8_kkvODL7F4neGLNGgdpFDm9pPzoYpwbQkc0BU5GVOFoq8CSmD0DDvDjG" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-gtm-yt-inspected-9="true" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p><h2 dir="ltr">Understanding Battery Health and Machine Learning</h2><p dir="ltr">Battery health, particularly in the context of electric vehicles and industrial batteries, refers to the capacity of a battery to store and deliver energy efficiently over its lifespan. It is a critical factor in the performance and longevity of electric vehicles and industrial applications that rely on battery power. Degradation in battery health can lead to reduced range in electric vehicles, frequent recharging, and battery replacement.<sup>2</sup></p><p dir="ltr">Machine learning, a subset of artificial intelligence, can be applied to monitor and improve battery health. It involves using algorithms and statistical models to perform tasks without explicit instructions, relying on patterns and inference. In battery health, machine learning can be used to analyze data from the battery and predict its health and lifespan.<sup>3</sup></p><p dir="ltr">The application of machine learning in battery health management is a rapidly evolving field. Machine learning algorithms can analyze vast amounts of data generated by batteries in real time, identifying patterns and making predictions that would be impossible for humans to do manually. This can lead to more accurate predictions of battery life, allowing for more efficient use of batteries and potentially extending their lifespan.</p><p dir="ltr">Furthermore, anomaly detection can help identify potential issues before they become serious problems. Identifying unusual patterns in battery data allows it to flag potential issues and address them proactively, preventing further damage and extending the battery's useful life.<sup>4</sup></p><p dir="ltr">Predictive maintenance takes this further, using data to predict when a battery may fail or need servicing. <sup id="isPasted">5</sup> This allows maintenance to be scheduled at opportune times, minimizing downtime and potentially extending the battery's life.<sup>6</sup> Integrating these machine learning concepts into battery health management represents a significant advancement in the field.</p><h2 dir="ltr">Challenges in Implementing Machine Learning for Battery Health</h2><p dir="ltr">Implementing machine learning for battery health monitoring and improvement is challenging. These technical and practical challenges can significantly impact the effectiveness of machine-learning applications in this field.</p><p dir="ltr">One of the primary challenges is the accurate prediction of battery life. This is a critical aspect of the business case for electric vehicles, stationary energy storage, and emerging applications such as electric aircraft. Existing methods for battery life prediction are based on relatively small but well-designed lab datasets and controlled test conditions. However, incorporating field data is crucial to understanding how cells age in real-world situations. This comes with additional challenges because end-use applications have uncontrolled operating conditions, less accurate sensors, data collection and storage concerns, and infrequent access to validation checks.<sup>7</sup></p><p dir="ltr">Another challenge is the variability in battery usage conditions. In a lab setting, the cycling pattern of batteries can be closely controlled, and regular reference performance tests can be performed to quantify health. However, field data from real-world applications exhibits irregular cycling patterns, varying operating conditions, and path-dependent degradation mechanisms, making reliable predictions difficult. This setting is extremely relevant for industrial needs, such as predicting the remaining useful life of a customer’s electric vehicle or compliance with warranty conditions for grid storage systems; prognostics using real-world data remains an open research challenge.</p><p dir="ltr">Moreover, implementing machine learning for battery health also faces challenges related to data requirements. The "curse of dimensionality" refers to the data needed to capture all combinations of operating conditions growing quickly with the number of investigated conditions. This is compounded by the relatively slow rate at which battery lifetime data can be acquired, taking several months or years of experiments for each change in chemistry, form factor, or manufacturing process.<sup>7</sup></p><p dir="ltr">While machine learning is promising for improving battery health monitoring and prediction, significant challenges remain. Overcoming these challenges will require innovative approaches that effectively combine machine learning techniques with physical models and leverage lab and field data.</p><h2>Edge Impulse: A Solution to the Challenges&nbsp;</h2><p>Edge Impulse is a leading platform in embedded machine learning, providing a comprehensive solution to the challenges faced in implementing machine learning for battery health monitoring and improvement. The platform is designed to handle real-world sensor data, making it particularly suited for battery health applications.</p><p dir="ltr">Edge Impulse addresses these challenges by providing a platform that optimizes models for any Edge device. This means that regardless of the specific hardware used in a battery system, machine learning models can be tailored to perform efficiently on that device. This flexibility is crucial in overcoming the technical challenges of implementing machine learning in diverse real-world scenarios.<sup>8</sup></p><p dir="ltr">Furthermore, Edge Impulse provides tools for advanced data analysis that can help build high-quality sensor datasets and quickly detect data quality issues. This is particularly relevant in battery health, where accurate and high-quality data are crucial for effective monitoring and prediction.</p><p dir="ltr">Edge Impulse has been used in various industries and applications to leverage machine learning for improved operational efficiency. For instance, Oura, a health technology company, used Edge Impulse to boost sleep-scoring accuracy by 17%.<sup>9</sup> While this example is not directly related to battery health, it demonstrates the potential of Edge Impulse's machine-learning solutions in real-world applications.</p><p dir="ltr">The predictive maintenance solutions offered by Edge Impulse could be used to predict when a battery might fail or need servicing, allowing for timely intervention and potentially extending the battery's life. Similarly, asset tracking and monitoring solutions could be used to monitor the health of batteries in real time, identifying potential issues before they become serious problems.&nbsp;</p><p dir="ltr">Edge Impulse offers a promising solution to the challenges of implementing machine learning for battery health. By providing a flexible platform that can handle real-world sensor data and optimize models for any edge device, Edge Impulse is paving the way for more effective and efficient battery health management.</p><h2 dir="ltr">Conclusion</h2><p dir="ltr">Battery health is crucial for electric vehicles and industrial applications' performance and longevity. Machine learning offers a promising solution for battery health management, despite challenges in accurate life prediction, variable usage conditions, and data requirements. Edge Impulse, a leading platform in embedded machine learning, addresses these challenges by providing a flexible platform for real-world sensor data and model optimization. As these technologies continue to evolve, they hold significant potential for improving battery performance and lifespan, marking a substantial advancement in the field.</p><p dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="540" height="600" src="https://c8056756.sibforms.com/serve/MUIFAMyA6BGksOUPEokHuBnEmZRHhYgJ4RXb4BV0gvzN98ymNIvQdTcBFQnRVfviT6yZSFW_R2g1hENBu7p7Afnyv9Kt0unAAbfFU5mMQhBwI-8hQnKf2r1mmRKhUgDLmtk8bpVSZ7kZGpleCPqtYRv8_kkvODL7F4neGLNGgdpFDm9pPzoYpwbQkc0BU5GVOFoq8CSmD0DDvDjG" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-gtm-yt-inspected-9="true" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><br></p><h2 dir="ltr">References</h2><p dir="ltr">[1] Dunn J. Battery State of Health – What is It? Why is It Important? The Equation. 2022 [cited 2023 Jul 27]. Available from:<a href="https://blog.ucsusa.org/jessica-dunn/battery-state-of-health-what-is-it-why-is-it-important/" target="_blank">&nbsp;https://blog.ucsusa.org/jessica-dunn/battery-state-of-health-what-is-it-why-is-it-important/</a><a href="https://chat.openai.com/c/5b60baf5-1652-4d90-bfeb-362ac40daad0#user-content-fnref-1%5E" target="_blank">&nbsp;</a><a href="https://chat.openai.com/c/5b60baf5-1652-4d90-bfeb-362ac40daad0#user-content-fnref-1%5E-2" target="_blank"></a></p><p dir="ltr">[2]: Battery University. (2023). How to Prolong Lithium-based Batteries. Retrieved from https://batteryuniversity.com/learn/article/how_to_prolong_lithium_based_batteries</p><p dir="ltr">[3]: Zhang, S., Zhang, C., Xiong, R., &amp; He, H. (2020). Review the management of battery aging based on the digital twin driven by artificial intelligence. Journal of Energy Storage, 30, 101634. doi:10.1016/j.est.2020.101634</p><p dir="ltr">[4]: Chandola, V., Banerjee, A., &amp; Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 1-58. doi:10.1145/1541880.1541882</p><p dir="ltr">[5]: Mobley, R. K. (2002). An introduction to predictive maintenance. Elsevier.</p><p dir="ltr">[6]: Sinha Roy, A. (2023). Predictive Maintenance: A New Way Ahead for Battery Management. Retrieved from <a href="https://www.spiceworks.com/tech/hardware/guest-article/battery-management-electric-vehicles/" target="_blank">https://www.spiceworks.com/tech/hardware/guest-article/battery-management-electric-vehicles/</a></p><p dir="ltr">[7] Samad NA, Kim Y, Siegel JB. The challenge and opportunity of battery lifetime prediction from field data. Joule. 2021;5(8):1-32. doi:10.1016/j.joule.2021.06.005</p><p dir="ltr">[8]: Edge Impulse. (2023). Optimize AI for the edge. Retrieved from https://www.edgeimpulse.com/</p><p dir="ltr">[9]: Edge Impulse. (2023). Learn how Edge Impulse helped Oura to boost sleep-scoring accuracy by 17%. Retrieved from https://assets-global.website-files.com/618cdeef45d18e4ef2fd85f3/628795dc61c12d55b0f4e769_Go-Deeper-On-Deep-Sleep-Oura.pdf</p>

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