Cloud to the Edge: Future of LLMs w/ Mahesh Yadav of Google

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#Tech #tinyML Foundation #TinyML #IoT #Edge #Genai #Semiconductors #Device #Edgeai #Communities

30:14

"It is a scientific fact that these macaques, like all other primates, including humans, are communicating. They communicate in much the same way we do - facial expressions, vocalizations, body postures, those kinds of things." - Jeff Kerr Jeff Kerr is PETA foundations Chief Legal Officer. I asked him to come on the show to talk about one of PETA’s current lawsuits against the National Institutes of Health (NIH) and Nathional Institute of Mental Health (NIMH). PETA is arguing that the monkeys being tested on in a government run facility are capable of communication (or “are communicating”). And that we have a constitutional right under the First Amendment to receive their communications. This could be a game changer in allowing us to see what’s really going on in labs that are funded by taxpayer money, and which have so far been censored from public view. PETA’s lawsuit follows years of NIH’s attempts to deny Freedom of Information requests banning PETA executives from its campus and illegally censoring animal advocates’ speech on NIH’s public social media pages. Through the lawsuit, PETA is seeking a live audio-visual feed to see and hear real-time communications from the macaques who have been kept isolated, used in fear experiments, and had posts cemented into their heads. Anthropologists and other scientists have studied macaque and other primate communications for decades and know that the monkeys communicate effectively and intentionally through lip smacking, fear grimaces, body language, and various cries and sounds—all of which constitute speech under the law. Primatologists can analyze that speech on a deeper level to share their stories with the world.…

about a year ago 59:48

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Curious about how you can run a colossal 405 billion parameter model on a device with a mere 2 billion footprint? Join us with Mahesh Yadav from Google, as he shares his journey from developing small devices to working with massive language models. Mahesh reveals the groundbreaking possibilities of operating large models on minimal hardware, making internet-free, edge AI a reality even on devices as small as a smartwatch. This eye-opening discussion is packed with insights into the future of AI and edge computing that you don't want to miss.
Explore the strategic shifts by tech giants in the language model arena with Mahesh and our hosts. We dissect Microsoft's investment in OpenAI’s Phi model and Google's development of Gamma, exploring how increasing the parameters in large language models leads to emergent behaviors like logical reasoning and translation. Delving into the technical and financial implications of these advancements, we also address privacy concerns and the critical need for cost-effective model optimization in enterprise environments handling sensitive data.
Advancements in edge AI training take center stage as Mahesh unpacks the latest techniques for model size reduction. Learn about synthetic data generation and the use of quantization, pruning, and distillation to shrink models without losing accuracy. Mahesh also highlights practical applications of small language models in enterprise settings, from contract management to sentiment analysis, and discusses the challenges of deploying these models on edge devices. Tune in to discover cutting-edge strategies for model compression and adaptation, and how startups are leveraging base models with specialized adapters to revolutionize the AI landscape.

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Chapters

1. Cloud to the Edge: Future of LLMs w/ Mahesh Yadav of Google (00:00:00)

2. Edge AI Development and Challenges (00:00:37)

3. Edge AI With Small Language Models (00:13:43)

4. Advancements in Edge AI Training (00:22:53)

5. Techniques for Model Size Reduction (00:27:15)

6. Applications of Small Language Models (00:37:40)

7. Discussion on NVIDIA, ONNX, and Acceleration (00:41:05)

8. Model Compression and Adaptation Techniques (00:53:57)

43 episodes

Cloud to the Edge: Future of LLMs w/ Mahesh Yadav of Google

EDGE AI POD

published about a year ago

MP3•Episode home

Send us a text

Support the show

Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

Chapters

1. Cloud to the Edge: Future of LLMs w/ Mahesh Yadav of Google (00:00:00)

2. Edge AI Development and Challenges (00:00:37)

3. Edge AI With Small Language Models (00:13:43)

4. Advancements in Edge AI Training (00:22:53)

5. Techniques for Model Size Reduction (00:27:15)

6. Applications of Small Language Models (00:37:40)

7. Discussion on NVIDIA, ONNX, and Acceleration (00:41:05)

8. Model Compression and Adaptation Techniques (00:53:57)

43 episodes

#Tech #tinyML Foundation #TinyML #IoT #Edge #Genai #Semiconductors #Device #Edgeai #Communities

All episodes

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1
Garbage In, Garbage Out - High-Quality Datasets for Edge ML Research 21:17

2 days ago21:17

21:17

The EDGE AI FOUNDATION's Datasets & Benchmarks Working Group highlights the rapid progress in neural networks, particularly in cloud-based applications like image recognition and NLP, which benefited greatly from large, high-quality datasets. However, the constrained nature of edge AI devices necessitates smaller, more efficient models, yet a lack of suitable datasets hinders progress and realistic evaluation in this area. To address this, the Foundation aims to create and maintain a repository of production-grade, diverse, and well-annotated datasets for tiny and edge ML use cases, enabling fair comparisons and the advancement of the field. They emphasize community involvement in contributing datasets, providing feedback, and establishing best practices for optimization. Ultimately, this initiative seeks to level the playing field for edge AI research by providing the necessary resources for accurate benchmarking and innovation. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

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Edge AI Investing Essentials 56:00

9 days ago56:00

56:00

The path to successful investment in edge AI requires far more than technical brilliance. In this revealing panel discussion, venture capitalists and corporate investors share what truly matters when deciding where to place their bets in the evolving edge computing landscape. At the heart of every investment decision lies a deceptively simple question: who is your customer? As Hans from Momentum Ventures bluntly states, "The first thing we're looking for is customers, second is customers, and third is customers." While many founders obsess over technology, successful investments begin with understanding whose job is changed by your solution and who will pay for that change. The conversation shifts to efficiency as "the new currency" in edge computing. David Wyatt, formerly of NVIDIA, highlights technologies achieving 100x greater efficiency than traditional approaches, pointing to innovations that challenge conventional silicon-based computing. Meanwhile, Murata's corporate venture team emphasizes material science innovations that enable more efficient processing, sensing, and power management at the edge. What makes an ideal founding team? The panel describes the powerful combination of a "hacker" (technical expert) and a "hustler" (business-focused leader) who together can bridge the gap between technological innovation and market demands. This complementary expertise proves especially critical in edge AI, where technical constraints meet real-world implementation challenges. The most sobering insights emerge when discussing startup failures. Running out of cash tops the list, often resulting from scaling too quickly or misallocating resources. One panelist cuts through the hype with brutal clarity: "You're not in business when you're spending money. You're in business when you're making money." Whether you're building, investing in, or partnering with edge AI companies, this discussion offers a roadmap for navigating an increasingly complex landscape where efficiency, customer focus, and strategic vision determine which innovations will ultimately survive and thrive. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

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Beyond the Edge: Cloud and AI Convergence 13:47

16 days ago13:47

13:47

Beyond the Edge , from the EDGE AI FOUNDATION, explores the future of edge computing by advocating for a shift in perspective. It suggests moving beyond the limitations of traditional IoT deployments by integrating advancements in edge AI, semiconductors, and connectivity. The author argues that the cloud will serve as a crucial "binding agent," enabling unified management and orchestration from the cloud down to edge devices. Instead of focusing on restrictive standards, the piece emphasizes the importance of developing best practices and fostering collaboration to accelerate the deployment and value of edge AI solutions. The ultimate vision is a future where AI-powered, connected silicon at the edge becomes the default, supported by cloud-based DevOps principles. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

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Investing In The Edge: A VC Panel from AUSTIN 2025 42:43

23 days ago42:43

42:43

Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

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The Pipeline Is Stalling: America's Declining Innovation Edge 13:41

30 days ago13:41

13:41

America's innovation pipeline stands at a dangerous crossroads. Federal research funding has dropped 10% in real terms over the past decade, traditional public-private collaboration models are fragmenting, and barriers to international talent continue to rise. These challenges threaten the three-pillar foundation that has powered American technological leadership for generations. Our conversation dives deep into Harvard University Professor Vijay Janapa Reddi's compelling analysis of this critical situation. The statistics are striking: researchers have lost 26% of their purchasing power since 2014, forcing tough choices about graduate positions, equipment purchases, and research directions. Meanwhile, 79% of computer science graduate students are international, underscoring our reliance on global talent. When 55% of billion-dollar American startups have immigrant founders, restrictive immigration policies amount to what experts call "national self-sabotage." The impacts extend far beyond elite institutions. America's innovation ecosystem encompasses land-grant universities, HBCUs, community colleges, and state flagships - all dependent on stable federal support. The Edge AI Foundation, where Professor Reddy serves on the board, exemplifies one promising response: creating structured collaboration between universities and industry on emerging technologies like neuromorphic computing and edge-based AI. This approach helps bridge the crucial gap between academic research and commercial application. Revitalizing our innovation ecosystem demands a coordinated strategy: expanded industry consortia with federal matching funds, innovation co-labs on university campuses, dedicated STEM green cards, streamlined visa processes, and predictable research funding increases. The stakes couldn't be higher - our economic future and technological leadership hang in the balance. How might you contribute to rebuilding America's innovation pipeline? Explore more through the People's Pledge for American Higher Education and join the conversation about securing our innovative future. ➡️ Visit HERE to read The Pipeline Is Stalling whitepaper ➡️ Visit HERE to sign the pledge Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

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Beyond the Edge - Analyst Insights at AUSTIN 2025 59:38

5 weeks ago59:38

59:38

What happens when four tech industry veterans sit down to dissect the future of Edge AI? A no-holds-barred conversation that cuts through the hype and delivers actionable insights for anyone working in this rapidly evolving field. The panelists tackle the burning question head-on: How can Edge AI avoid becoming the "box of cables in your garage" that IoT transformed into? Through candid discussion, they reveal that success depends less on technical prowess and more on solving real business problems with clear ROI. As one panelist bluntly states, "The customer doesn't care about MQTT or AMQP protocols—nobody cared. They care about outcomes." The conversation weaves through crucial territory—the boundary between IT and OT systems, the evolving relationship between cloud and edge architectures, and the stark reality of skills shortages in the semiconductor industry. With semiconductor companies doubling in size over the next eight years while facing an aging workforce, the panel highlights urgent needs for reskilling and education initiatives. Perhaps most provocatively, the panel addresses the uncomfortable truth about AI adoption: "How many employees is your technology going to replace?" This question, frequently asked by VCs, underscores the economic drivers pushing Edge AI forward in an era of changing workforce demographics and productivity challenges. For small and medium businesses, there's both opportunity and risk as AI becomes embedded in SaaS offerings and purpose-built solutions emerge for specific industry problems. The panelists predict that Edge AI might actually benefit smaller players more than large enterprises by democratizing access to sophisticated capabilities through platform-based approaches. Whether you're developing Edge AI solutions, investing in the space, or considering adoption for your business, this discussion provides essential context for navigating the significant opportunities and challenges ahead. Subscribe to hear more industry experts cut through the noise and deliver practical wisdom for the intelligent edge. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

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Dan Cooley of Silicon Labs - The 30 Billion Dollar Question: Can AI Truly Live on the Edge? 23:59

6 weeks ago23:59

23:59

Imagine a world where your smart glasses don't just identify objects but tell stories about what they see—all while running on a tiny battery without heating up. This cutting-edge vision is becoming reality as semiconductor companies tackle the monumental challenge of bringing generative AI capabilities from massive cloud data centers down to microcontroller-sized devices. The semiconductor industry stands at a fascinating crossroads where artificial intelligence capabilities are pushing beyond traditional cloud environments into battery-powered edge devices. As our podcast guest explains, this transition faces substantial hurdles: while cloud-based models expand from millions to trillions of parameters, embedded systems must dramatically reduce their footprint from terabytes to gigabytes while still delivering meaningful AI functionality. With projections showing IoT devices consuming over 30 terabit hours of power by 2030 and generating 300 zettabytes of data, the need for local processing has never been more urgent. For developers creating wearable technology like smart eyewear, constraints become particularly challenging. Weight distribution, battery life, and computing power must all be carefully balanced while maintaining comfort and style. The hardware architecture required for these applications demands innovative approaches: shared bus fabrics that enable different execution environments, strategic power management that activates high-performance cores only when needed, and neural processing units capable of handling transformer operations for generative AI workloads. Most impressively, current implementations demonstrate YOLO object detection running at just 60 milliamps—easily within battery operation parameters. The $30 billion embedded AI market represents a tremendous opportunity for innovation, but also requires robust software ecosystems that help traditional microcontroller customers without AI expertise navigate this complex landscape. As next-generation devices begin supporting generative capabilities alongside traditional CNN and RNN networks, we're witnessing the dawn of truly seamless human-machine interfaces. Ready to explore how these technologies might transform your industry? Listen now to understand the future of computing at the edge. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

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The Future of Domain-Specific AI Search Lies in Targeted Agent Systems 1:00:46

7 weeks ago1:00:46

1:00:46

Imagine your edge device having the ability to search for exactly what you need, exactly when you need it, without hallucinations or irrelevant information. That's the promise of Snipe Search's agent orchestration system, presented by co-founder Wassim Kezai in this eye-opening EDGE AI TALKS session. Most organizations struggle when implementing RAG systems with their corporate data. The truth is, unstructured corporate knowledge is often messy and inconsistent, leading to unreliable AI responses. Semantic matching issues in traditional retrieval systems further compound these problems, especially when deployed at the edge where specific, accurate information is crucial. Wasim unveils an innovative approach that deploys specialized AI "detective" agents to search for information from authoritative sources. Unlike brute force search methods, these agents intelligently target reliable information based on hierarchical importance. Web agents crawl and cross-reference websites, image agents find relevant visuals, scholar agents specialize in academic information, and video agents can even pinpoint the exact timestamp in video content that answers your query. What sets this approach apart is its adaptability to domain-specific knowledge and verification frameworks. Companies can customize how information is validated based on their standards, ensuring relevance and accuracy. While traditional RAG systems respond in seconds, Snipe Search's 30-second average response time delivers significantly higher quality information – a worthwhile trade-off for mission-critical applications. The platform integrates easily with any LLM or chatbot through Docker, API, or direct integration, making it accessible for organizations of all sizes. As edge computing continues to grow, having efficient, accurate search capabilities becomes increasingly important for reducing cloud dependencies, enhancing privacy, and delivering better user experiences. Ready to transform how your edge devices access and utilize knowledge? Explore Snipe Search's platform launching in the coming weeks and discover how intelligent search can enhance your edge AI deployments. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

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Cleaning Our Oceans with Edge AI 58:47

8 weeks ago58:47

58:47

The crisis of ocean plastic pollution demands innovative solutions, and Brad's team at Ozone Technologies is answering the call with cutting-edge AI technology. Their collaboration with The Ocean Cleanup has yielded a remarkable system that's transforming how we detect and map marine debris. At the heart of this solution is ADIS (Autonomous Debris Imaging System) – a compact, rugged camera powered by edge AI that mounts to merchant vessels traveling across our oceans. Using sophisticated computer vision models running on NXP i.MX 8M Plus processors, these devices scan the water's surface, distinguishing tiny pieces of floating plastic from wave crests even in challenging marine conditions. What makes this approach revolutionary is its scalability. Rather than requiring dedicated research vessels, ADIS piggybacks on existing shipping routes, creating an expanding network of detection points across global waters. The system operates completely autonomously, requiring minimal power and no continuous internet connection. It stores detection data onboard, uploading it only when ships return to port. The technical challenges overcome by Brad's team are impressive – from waterproofing the hardware to withstand immersion and pressure spray to developing specialized tracking algorithms that can differentiate persistent floating debris from transient wave patterns. Perhaps most remarkable is how they've simplified deployment with fool-proof installation instructions that require no technical expertise from ship crews. This mapping data serves a critical purpose, helping The Ocean Cleanup optimize their System 3 recovery operations in the Great Pacific Garbage Patch. Their massive 2.2km floating barrier (nicknamed "Josh") funnels debris into collection zones, but knowing where to deploy is essential for efficiency. ADIS provides that critical intelligence. Beyond mapping, we're seeing the completion of a virtuous cycle as recovered plastic finds new life through creative recycling – including limited-edition Coldplay vinyl records made from ocean plastic. Want to see more innovations tackling our planet's pressing environmental challenges? Join us at the upcoming AJI Foundation Summit in Austin or explore edgifoundation.org/events for opportunities to connect with leaders in this space. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

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Smart Chips, Big Dreams: How NXP is Changing the AI Game 58:31

9 weeks ago58:31

58:31

The artificial intelligence landscape is undergoing a fundamental shift – moving away from simply compressing cloud models to fit edge devices toward developing AI that's truly "born at the edge." Davis Sawyer, AI Product Marketing Manager at NXP Semiconductors, guides us through this transformation and reveals how NXP is revolutionizing the way intelligence is built into our everyday devices. Sawyer begins by redefining what we mean by "the edge" – a vast spectrum ranging from network infrastructure handling millions of connections down to the dozens of microcontrollers in modern home appliances and vehicles. What makes edge AI unique isn't just about size constraints, but the fundamentally different operating environment. While cloud models enjoy virtually unlimited power and standardized computing environments, edge devices face strict limitations in form factor, power consumption, and thermal management. The heart of NXP's approach is their EIQ software stack – a comprehensive toolkit that spans their entire product range from low-power MCUs to high-performance MPUs. Two innovations stand out as particularly revolutionary: Time Series Studio brings AutoML capabilities to sensor data, enabling non-AI experts in manufacturing, energy, and other sectors to build powerful anomaly detection models without deep machine learning expertise. Meanwhile, their approach to generative AI uses "RAG on steroids" (Retrieval Augmented Generation) to create systems that are not only compact enough for edge deployment but also inherently more secure and private. The real-world impact is already evident in applications ranging from precision agriculture robots to healthcare systems that combine multimodal sensing for contact-free patient monitoring. Perhaps most impressive is the rapid pace of innovation – within just months, NXP's edge-optimized language models have seen response times drop from two seconds to less than half a second, making conversational interfaces truly viable on embedded devices. Looking ahead, Sawyer predicts we're moving toward a new era where edge AI becomes increasingly agentic – focusing not just on human-machine interfaces but on optimizing machine-to-machine workflows in factories, robotics, and automation. Join us to discover how the future of intelligence isn't trickling down from the cloud, but rising up from the edge where our data is born. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

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Counting What Counts: How Edge AI is Saving Japan's Seafood Industry 59:18

10 weeks ago59:18

59:18

When SoftBank's team was approached by Japanese fishermen struggling with declining catches and inefficient practices, they knew technology could offer a solution. Their response: an innovative Edge AI system that transforms traditional aquaculture through sophisticated computer vision running directly on smartphones. Japan's fishing challenges are emblematic of global food security concerns. With the country's self-sufficiency rate at just 38% and fish catches halved from peak levels, the stakes couldn't be higher. Traditional aquaculture has relied heavily on intuition rather than data, making it nearly impossible to optimize feeding (which represents 60-70% of operational costs) or accurately monitor fish populations. The SoftBank and AIZip collaboration tackles these challenges through a remarkable edge computing approach. What makes their solution particularly groundbreaking is how it functions in environments with zero infrastructure—no power supply, no connectivity, and corrosive saltwater everywhere. By developing density-based crowd counting AI models that run efficiently on edge devices, they've created a system that can count hundreds of fish with 96% accuracy in clear water and 86% accuracy in muddy conditions. Perhaps most fascinating is their development process. Unable to collect sufficient real-world training data underwater, the team developed a sophisticated Unity-based simulation that generates realistic fish behavior under various conditions. This simulator provided 65% of their training data, complemented by manual observation from divers who documented actual fish behavior at different depths and feeding stages. The result is an AI system that not only counts fish but can potentially detect hunger levels, health issues, and optimize feeding schedules. This CES 2024 Innovation Award-winning technology demonstrates how Edge AI can transform traditional industries without requiring massive infrastructure investments. By bringing intelligence directly to the point of data collection, even in the most challenging environments, we're witnessing the beginning of a new era in sustainable food production. Whether you're involved in agriculture, environmental monitoring, or any field requiring intelligent sensing in harsh conditions, the lessons from this underwater AI revolution could transform how you approach your next challenge. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

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Career EDGE: Navigating the Job Market in the Age of Edge AI 1:28:56

11 weeks ago1:28:56

1:28:56

From 5V Tech and the EDGE AI FOUNDATION, unlock the potential of Edge AI and transform your career path with insights from leaders in the field. Join our engaging conversation featuring Luke Perrins from 5V, Gregory from Infineon Technologies, and Professor Eiman Kanjo from Nottingham Trent University, as we uncover how Edge AI is revolutionizing industries with its unique ability to operate independently of cloud connectivity. Learn from seasoned professionals like Stephen Davis, an executive leadership coach, Kari, a corporate recruiter, and Martin MacDonald, COO at AI startup Weteeq, about the innovative applications and benefits of this technology, including improved latency and enhanced device autonomy. Explore the diverse and dynamic skill set essential for success in the Edge AI realm. Our experts discuss the importance of mastering embedded software, programming, and hardware understanding, while also emphasizing cross-disciplinary collaboration and soft skills like communication. Whether you're a graduate or a seasoned professional, discover actionable advice on crafting standout resumes and CVs tailored to this evolving field, as well as strategies to showcase your personal projects and achievements effectively. Prepare to stand out in your job applications and interviews with expert tips on leveraging networking opportunities and professional platforms like LinkedIn. Discover the critical role of personal branding and cultural understanding, and gain insights into future careers in Edge AI, where versatility and innovation are key. Tune in to this insightful episode packed with practical strategies to help you excel in the cutting-edge world of Edge AI. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

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Revolutionizing Wi-Fi Sensing with Machine Learning and Advanced Radio Frequency Techniques 13:24

12 weeks ago13:24

13:24

What if the future of Wi-Fi could pinpoint your location down to 30 centimeters? Join us as Joseph Chueh from National Tsinghua University unveils the astonishing potential of Wi-Fi sensing when integrated with machine learning. Joseph brings his wealth of experience in semiconductor research and business development to the table, discussing the revolutionary application of existing frequencies like 2.4G and 5G for tasks including human activity recognition and intruder detection. This episode unpacks how Channel State Information (CSI) is at the heart of extracting precise data for machine learning, while also addressing the technical hurdles of hardware optimization and interference management. Discover how increasing the degrees of freedom in Wi-Fi systems can be a game-changer for radio frequency technology. Joseph explains how adding more channels or phase coordination expands the sample space for channel information, paving the way for more efficient decision-making. We explore solutions like transmitter-side coding and the impact of transmission models like OFDM and OFDMA on Wi-Fi sensing capabilities. Joseph paints a vivid picture of a future where Wi-Fi sensing becomes not only more accurate but also more cost-effective and accessible, making it a promising feature in both today's Wi-Fi technologies and upcoming 6G systems. Whether for robotics or enhancing room-scale environments, the insights shared in this episode offer a glimpse into an exciting wireless frontier. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

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From Ideas to Reality with Edge AI Expertise from embedUR and ModelNova 59:56

13 weeks ago59:56

59:56

How can we revolutionize everyday objects into smarter, more responsive entities? Join us in this exciting episode as we explore the cutting-edge world of Edge AI, fresh from the vibrant showcases at CES 2025. We promise an in-depth look at the future of technology deployment with our special guests Eric and John from EmbedUR. With their extensive expertise in embedded products, they bring a fascinating perspective on the importance of blueprints for tech commercialization in today's rapidly evolving landscape. Our conversation with Eric Smiley and John Marconi introduces the innovative ModelNova, a transformative tool designed to speed up the journey from ideation to proof of concept for tiny edge AI devices. Discover how ModelNova's curated model zoo and datasets can empower developers, ensuring efficient performance even under resource constraints. We dive into practical insights like community contributions, adapting large AI models for small devices, and how tools like Model Nova democratize access across various chip platforms, turning ambitious ideas into reality. This episode doesn't just stop at the technical nuances; it goes further into the realm of edge AI product development. We share a compelling story of transforming a bicycle helmet camera with object detection to enhance rider safety, illustrating the complexities of selecting the right hardware and the critical role of blueprints in this journey. From MLOps integration to cloud connectivity for continuous updates, our discussion emphasizes collaboration within the tech ecosystem to tackle the challenges of AI deployment. Tune in to learn how these advancements are not only reshaping technology but also enriching everyday life by making objects around us smarter and more responsive. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

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Reimagining Edge AI using Breakthrough Tools with Ali Ors of NXP 16:00

14 weeks ago16:00