Garbage In, Garbage Out - High-Quality Datasets for Edge ML Research

EDGE AI POD

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محتوای ارائه شده توسط EDGE AI FOUNDATION. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط EDGE AI FOUNDATION یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

Accidental CEO Podcast

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72: If You Want to Grow—Stop Fixing the Wrong Problem 16:32

27 روز پیش16:32

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16:32

You’re busy—but are you actually growing? In this episode, Nata Salvatori exposes a trap that’s costing service providers time, money, and sanity: chasing busywork that feels productive but doesn’t move the needle. She walks through a clear, five-step growth path—from clarifying your offer, validating through real sales, delivering sustainably, building repeatable systems, to scaling confidently. You’ll learn: How to spot and ditch “fake work” Why clarity beats complexity every time How to use real feedback to validate your offers Delivery tips that prevent burnout System creation that enables scaling How to honor your current phase of growth 📌 Ready to stop spinning your wheels and make real moves? Map your phase, pick your next action, and don’t be afraid to ask for help: 👉 accidentalceo.co/coaching Support the show…

حدود یک سال پیش 21:17

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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.

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Support the show

Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

فصل ها

1. Introduction to Edge AI Challenges (00:00:00)

2. Cloud vs Edge: Different Data Needs (00:01:55)

3. The Problem with "Toy Examples" (00:04:03)

4. Edge AI Foundation's Repository Solution (00:06:30)

5. Technical Requirements Framework (00:09:06)

6. Data Quality Strategy and Focus Areas (00:12:22)

7. Community Participation and Call to Action (00:15:30)

8. Key Takeaways and Future Impact (00:18:12)

51 قسمت

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

Garbage In, Garbage Out - High-Quality Datasets for Edge ML Research

EDGE AI POD

published حدود یک سال پیش

اشتراک گذاری

MP3•خانه قسمت

Send us a text

Support the show

Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

فصل ها

1. Introduction to Edge AI Challenges (00:00:00)

2. Cloud vs Edge: Different Data Needs (00:01:55)

3. The Problem with "Toy Examples" (00:04:03)

4. Edge AI Foundation's Repository Solution (00:06:30)

5. Technical Requirements Framework (00:09:06)

6. Data Quality Strategy and Focus Areas (00:12:22)

7. Community Participation and Call to Action (00:15:30)

8. Key Takeaways and Future Impact (00:18:12)

51 قسمت

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

همه قسمت ها

EDGE AI POD

1
How embedUR is bridging the gap between embedded development and AI with Rajesh Subramanian 17:49

۶ hours پیش17:49

17:49

The technological pendulum has swung dramatically over the last two decades. From desktop computing to cloud dominance and now back to the critical importance of edge devices, we're witnessing a renaissance in embedded systems. But this time, they're getting smart. Rajesh, founder of embedUR, takes us on a journey through this evolution, explaining how his company transformed from connectivity specialists to edge AI innovators. Founded in 2004 to accelerate embedded product development, EmbedUR has positioned itself at the fascinating intersection where traditional embedded engineering meets artificial intelligence. This convergence creates unique challenges – embedded developers understand hardware constraints while AI engineers work in high-level abstractions. Bridging this gap requires careful training, collaboration, and a deep understanding of both worlds. The real magic happens when we see edge AI in action. Imagine headphones that filter background noise without cloud connectivity, privacy sensors that recognize you without capturing detailed facial images, or coffee machines that remember your preferences just by detecting your presence. These aren't futuristic concepts but working demonstrations EmbedUR has created with partners like STMicro, Synaptics, and NXP. What makes these implementations particularly valuable is their independence from cloud connectivity, enhancing both privacy and security. Yet commercializing edge AI presents significant hurdles. The journey from a 96% accurate demo to a 99% reliable product involves months of testing across diverse environments and user populations. As Rajesh points out, "Your model is only as good as your dataset," highlighting the critical importance of data curation. Through their Model Nova platform and global partnerships, EmbedUR is helping companies navigate this complex transition from prototype to market-ready product. Ready to explore how intelligence at the edge might transform your industry? The revolution is already underway. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

EDGE AI POD

1
How Weeteq is Revolutionizing Microcontroller Technology for Industrial Automation 20:30

۵ روز پیش20:30

20:30

What happens when you push AI to the absolute edge of networks? Meet Weeteq, a Scottish startup revolutionizing system optimization by bringing intelligence directly into microcontroller control loops. In this fascinating conversation with Martin from Weeteq, we explore the concept of "Ultra Edge" - technology that operates beyond what we typically consider Edge AI. Unlike conventional approaches that process data at the device level, WeTech's innovation works within the microcontroller itself, detecting and responding to error signals in less than one-tenth of a millisecond. This isn't just incremental improvement; it's challenging fundamental assumptions about what's possible at the lowest levels of our technology stack. The implications are profound across multiple industries. For industrial automation, WeTech's technology optimizes motor performance without requiring expensive hardware replacements. In electric vehicles, it extends range by reducing energy consumption and minimizing stress on traction motors. Even safety-critical systems in autonomous vehicles benefit from their comprehensive monitoring capabilities. All this comes from a lightweight implementation that works with existing infrastructure - no ripping out and replacing required. Martin also shares insights into Scotland's vibrant technology ecosystem, tracing his journey from Honeywell to startup founder and highlighting how universities like Strathclyde are fueling innovation in everything from robotics to renewable energy. It's a reminder that groundbreaking technological advances aren't limited to Silicon Valley or other well-known tech hubs. Ready to discover how ultra-edge computing might transform your industry? Listen now and glimpse the future of real-time system optimization. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

EDGE AI POD

1
From Cloud to Edge: NXP's AI Journey with Ali Ors 20:40

12 روز پیش20:40

20:40

The line between cloud and edge AI is blurring, and NXP Semiconductors stands at the forefront of this transformation. In this illuminating conversation, Ali Ors, Head of AI Strategy and Technologies at NXP, unveils how the semiconductor giant is embedding dedicated AI acceleration across their entire product portfolio—from basic microcontrollers to sophisticated application processors. With nearly a decade in the AI space, NXP has developed a comprehensive approach that doesn't position edge against cloud, but rather sees them as complementary forces. "We're not trying to make AI easy," Trescot explains, "we're trying to make it easier" for developers to harness AI capabilities in resource-constrained environments. This philosophy drives their hardware development and software enablement strategy, allowing customers across automotive, industrial, and IoT sectors to deploy sophisticated AI solutions where they're needed most. Perhaps most fascinating is NXP's advancement in bringing generative AI to the edge. Their imx9.5 processor now supports Large Language Models and Vision Language Models with up to 8 billion parameters, shifting the primary constraint from computation to memory limitations. This capability is already finding practical applications in conversational interfaces and scene understanding across industries, where hallucination-free, fact-based responses are non-negotiable. With their pending Kinara acquisition and active participation in the Edge AI Foundation, NXP continues to expand their ecosystem and push the boundaries of what's possible at the intelligent edge. Curious about how edge AI could transform your industry? Dive into this episode to discover the silicon that's making it happen. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

EDGE AI POD

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From Arduino to AI: A 20-Year Journey with David Cuartielles 22:57

19 روز پیش22:57

22:57

Twenty years after its founding in a small Italian café, Arduino has transformed from a simple open-source project into a global technology enabler that's educated an entire generation of makers, developers, and engineers. In this fascinating conversation with Arduino co-founder David Cuartielles, we explore the evolution of this groundbreaking platform and its growing role in the edge AI revolution. David takes us through Arduino's remarkable journey, sharing how a project named after an Italian king (and a local coffee shop) has grown to become the foundation of countless innovations worldwide. What began as an attempt to democratize basic electronics—making it less expensive and complicated "to just do something that would detect pressing a button and turning on a light"—has evolved into sophisticated platforms supporting edge AI and tiny machine learning applications. The conversation reveals how Arduino has kept pace with technological advancement, moving from basic microcontrollers to powerful Linux computers that can run complex applications and be updated over the air. We explore the practical applications of edge AI in industrial settings, where Arduino enables cost-effective, incremental improvements that deliver significant impacts. As David explains, sometimes the simplest implementations—like smart lamps or Roomba vacuum robots—prove most effective in the real world. Particularly inspiring is Arduino's commitment to global education, with David sharing insights from a recent DyniML workshop in Malawi that showcased the extraordinary talent and enthusiasm of participants. From manufacturing facilities in Italy and India to offices in Austin and educational initiatives across continents, Arduino continues to democratize technology access worldwide. Whether you're a longtime Arduino user, an AI enthusiast interested in edge computing, or simply curious about how small technologies can create massive impact, this episode offers valuable insights into the past, present, and future of one of technology's most influential platforms. Subscribe now and join us as we explore how Arduino continues to empower creators and innovators across the globe. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

EDGE AI POD

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From Sensors to Solutions: The Future of Edge AI with Chad Lucien of Ceva 17:06

26 روز پیش17:06

17:06

At the crossroads of cutting-edge technology and practical innovation stands Ceva, a semiconductor IP powerhouse with a remarkable two-decade legacy. Powering nearly 20 billion devices worldwide and shipping over 2 billion annually, SIVA has emerged as a crucial enabler in the burgeoning edge AI ecosystem. What distinguishes Ceva in this competitive landscape is their holistic approach to edge computing. Rather than focusing solely on neural processing, they've strategically built solutions around what Chad Lucien describes as the three pillars of edge AI: connectivity, sensing, and inference. This comprehensive vision has positioned them as the industry's leading Bluetooth IP licensor while developing sophisticated DSP solutions and a scalable NPU portfolio that ranges from modest GOPS to an impressive 400 TOPS. The secret to Ceva's effectiveness lies in their deep integration of hardware and software expertise. "The software is becoming the definition of the product," notes Lucien, explaining how their deep learning applications team directly influences hardware specifications. This software-first perspective has created solutions perfectly tailored for low-power, small form factor devices across diverse applications. From earbuds and health trackers to consumer robots and smart appliances, SIVA's fully programmable solutions handle everything from neural network computation to DSP workloads and control code. Most exciting is SIVA's leadership in the Audio ML renaissance through their work with the EDGE AI FOUNDATION's Audio Working Group. As audio applications shift from traditional DSP implementations to neural strategies, we're witnessing transformative capabilities in speech enhancement, anomaly detection, sound identification, and edge-based natural language processing. Discover how Ceva is providing the essential "picks and shovels" for the AI gold rush and why collaboration remains the key to unlocking the full potential of intelligence at the edge. Subscribe to hear more partner stories shaping the future of edge AI! Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

EDGE AI POD

1
EDGE AI Partner: David Aronchick of Expanso 21:51

۵ weeks پیش21:51

21:51

The digital landscape is rapidly evolving beyond centralized cloud computing. In this illuminating conversation with David Aronchik, co-founder of Expanso, we explore the growing necessity of processing data right where it's generated—at the edge. Drawing from his impressive background as the first non-founding PM for Kubernetes at Google and his leadership in open AI strategy at Microsoft, David reveals how these experiences led him to tackle a persistent challenge: how do you leverage container technologies and ML models outside traditional data centers? While cloud platforms excel at centralized workloads, businesses increasingly need computing power in retail locations, manufacturing facilities, and smart city infrastructure. Expanso's elegantly named Bacalhau project (Portuguese for cod, a clever nod to "Compute Over Data") offers a solution by providing reliable orchestration of workloads across distributed locations. Their lightweight Go binary runs on virtually anything from Raspberry Pis to sophisticated edge servers, managing the delivery and execution of jobs while gracefully handling connectivity disruptions that would cause traditional systems to fail. David makes a compelling case for edge computing with a simple physical reality: even 100,000 years from now, the speed of light will still impose a 45-millisecond latency between LA and Boston. This unchangeable constraint, combined with data transfer costs and regulatory requirements, makes local processing increasingly essential. For organizations struggling with high telemetry bills, Expanso confidently promises at least 25% cost reduction—or they work for free. Whether you're managing satellite networks, underwater cameras for aquaculture, or thousands of retail locations, this conversation illuminates how the future of computing involves bringing intelligence to where data lives rather than constantly shipping bytes across networks. Join us to discover how this paradigm shift is making AI more effective in the physical world. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

EDGE AI POD

1
Bringing Generative AI to Your Pocket: The Future of Edge Computing 27:10

۶ weeks پیش27:10

27:10

A technological revolution is quietly unfolding in your pocket. Imagine your phone creating stunning images, understanding what its camera sees, and responding to complex questions—all without sending a single byte of data to the cloud. This isn't science fiction; it's Generative EDGE AI, and it's already here. We dive deep into this transformative trend that's bringing AI's creative powers directly to our devices. Building on the foundation laid by the tiny ML movement, Generative EDGE AI represents a fundamental shift in how we'll interact with technology. The benefits are compelling: complete privacy as your data never leaves your device, lightning-fast responses without internet latency, independence from network connections, and significant cost savings from reduced cloud computing needs. The applications span far beyond convenience. For people with disabilities, it means having image captioning that works anywhere, even without internet. For photographers, it's like having a professional editor built right into your camera. In healthcare, it enables diagnostics while keeping sensitive patient data secure and accessible even in areas with poor connectivity. The technical achievements making this possible are equally impressive. Researchers have shrunk massive AI models to run efficiently on everyday devices, from visual question answering systems that respond in milliseconds to text-to-speech engines that sound remarkably natural. They're even making progress bringing text-to-image generation and small language models directly to smartphones. As we explore these breakthroughs, we consider the profound implications of truly intelligent devices that can learn, adapt, and make decisions autonomously. What happens when our technology not only understands but creates and acts independently? The silent AI revolution happening in our hands is set to transform our relationship with technology in ways we're just beginning to comprehend. Ready to understand the future that's already arriving? Listen now and glimpse the world where intelligence lives at your fingertips, not in distant server farms. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

EDGE AI POD

1
Audio AI on the Edge with Ceva 59:47

۷ weeks پیش59:47

59:47

Audio processing at the edge is undergoing a revolution as deep learning transforms what's possible on tiny, power-constrained devices. Daniel from SIVA takes us on a fascinating journey through the complete lifecycle of audio AI models—from initial development to real-world deployment on microcontrollers. We explore two groundbreaking applications that demonstrate the power of audio machine learning on resource-limited hardware. First, Environmental Noise Cancellation (ENC) addresses the critical need for clear communication in noisy environments. Rather than accepting the limitations of traditional approaches that require multiple microphones, SIVA's single-microphone solution leverages deep neural networks to achieve superior noise reduction while preserving speech quality—all with a model eight times smaller than conventional alternatives. The conversation then shifts to voice interfaces, where Text-to-Model technology is eliminating months of development time by generating keyword spotting models directly from text input. This innovation allows manufacturers to create, modify, or rebrand voice commands instantly without costly data collection and retraining cycles. Each additional keyword requires merely one kilobyte of memory, making sophisticated voice interfaces accessible even on the smallest devices. Throughout the discussion, Daniel reveals the technical challenges and breakthroughs involved in optimizing these models for production environments. From quantization-aware training and SVD compression to knowledge distillation and framework conversion strategies, we gain practical insights into making AI work effectively within severe computational constraints. Whether you're developing embedded systems, designing voice-enabled products, or simply curious about the future of human-machine interaction, this episode offers valuable perspective on how audio AI is becoming both more powerful and more accessible. The era of intelligent listening devices is here—and they're smaller, more efficient, and more capable than ever before. Ready to explore audio AI for your next project? Check out SIVA's YouTube channel for demos of these technologies in action, or join the Edge AI Foundation's Audio Working Group to collaborate with industry experts on advancing this rapidly evolving field. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

EDGE AI POD

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

۸ weeks پیش21:17

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…

EDGE AI POD

1
Edge AI Investing Essentials 56:00

۹ weeks پیش56:00

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…

EDGE AI POD

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Beyond the Edge: Cloud and AI Convergence 13:47

10 weeks پیش13:47

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…

EDGE AI POD

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

11 weeks پیش42:43

42:43

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

EDGE AI POD

1
The Pipeline Is Stalling: America's Declining Innovation Edge 13:41

12 weeks پیش13:41

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…

EDGE AI POD

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

13 weeks پیش59:38

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…

EDGE AI POD

1
Dan Cooley of Silicon Labs - The 30 Billion Dollar Question: Can AI Truly Live on the Edge? 23:59

14 weeks پیش23:59

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…

EDGE AI POD

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

15 weeks پیش1:00:46

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…

EDGE AI POD

1
Cleaning Our Oceans with Edge AI 58:47

16 weeks پیش58:47

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…

EDGE AI POD

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

17 weeks پیش58:31

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…

EDGE AI POD

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

18 weeks پیش59:18

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…

EDGE AI POD

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

19 weeks پیش1:28:56

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

20 weeks پیش13:24

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…

EDGE AI POD

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

21 weeks پیش59:56

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…

EDGE AI POD

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

22 weeks پیش16:00

16:00

NXP Semiconductors is revolutionizing the integration of AI within embedded systems, focusing on enhanced tools and dedicated acceleration. Their latest innovations like the EIQ Software Stack and EIQ Time Series Studio are designed to streamline AI deployment, bridging the gap for developers transitioning into AI methodologies. • Overview of NXP’s focus on automotive, industrial, and smart home markets • Introduction of new AI-accelerated MCUs and application processors • The importance of effective data management and compute efficiency • Discussion of the EIQ Software Stack and EIQ Toolkit for developers • Insights into the EIQ Time Series Studio for automated model generation • Highlights of challenges faced by developers in AI implementation • Key takeaways on NXP's commitment to enhancing edge AI capabilities Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

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Revolutionizing AI Deployment with Open-Source Edge Technologies with Odin Chen of Arm 14:49

23 weeks پیش14:49

14:49

What if you could deploy AI solutions seamlessly across every platform with just a single stroke? Join us as we explore this intriguing possibility alongside our guest, Odin Chen of Arm, who brings a wealth of experience from his journey from Taiwan to Cambridge. Together, we embark on a journey through the vibrant landscape of Edge AI, tackling its transformative impact on both enterprises and startups. We shine a light on the mantra "write once, deploy everywhere," unraveling its significance in a world where AI's reach is rapidly expanding. Along the way, we confront the challenges of AI deployment, from soaring costs to energy demands, and celebrate the critical role of open-source projects. Discover how technologies spanning image classification to generative AI are revolutionizing industries, while open-source contributions, particularly in Arm architecture and Python optimizations, are paving the way for a more collaborative future. In the second segment, we traverse the vast potential of the AI ecosystem, shedding light on AI frameworks like MediaPipe and ESSEC torch that guide industries toward optimized AI solutions. With Google's AI offerings on the horizon, we introduce a cutting-edge context model for ESSEC torch and delve into key partnerships with Arm technology pioneers such as Alif and HiMAS. These collaborations aim to turbocharge scalable computing solutions and high-efficiency image processing. Excitingly, we unveil our developer program and open software stack, inviting listeners to engage actively and collaborate. Keep your calendars marked for upcoming live demos and panel discussions, promising a firsthand look at the immense potential of AI deployment across diverse sectors. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

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Revolutionizing Resource-Constrained Devices with Cutting-Edge HIMAX Edge AI Processors 16:41

24 weeks پیش16:41

16:41

Unlock the fascinating world of edge AI technology with insights from Karan Kapoor of Himax Technologies. Discover how Karan and his team are pioneering the development of ultra-low-power AI processors designed for resource-constrained devices. We'll explore their latest innovation, the WE2 processor, which optimizes the balance between model size, speed, energy efficiency, and accuracy. Karan shares the intricacies of integrating hardware and software systems, along with groundbreaking AI applications like facial recognition and keyword spotting, all while staying true to Hymax's mission of making AI cost-effective and accessible for all. In the second segment, we spotlight a transformative partnership that brings Wwiseit2 into Edge Impulse's AI ecosystem. This collaboration is a game-changer, introducing a no-code platform that simplifies data collection and synthetic data generation. We unravel the process from model design to testing, supported by an AutoML feature that ensures precision. With ARM's Ethos U55 powering the AI processor, the episode explores how partnerships with innovators like GrowVision AI are crafting an advanced ecosystem. Developers, stay ahead of the curve by following these cutting-edge developments on platforms like LinkedIn and GitHub. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

EDGE AI POD

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Renesas and Reality AI: Shaping the Future of Edge Computing 13:15

25 weeks پیش13:15

13:15

Discover the cutting-edge world of TinyML and Edge AI with our special guest, Eldar Sido from Renesas. As a key figure in the AI Center of Excellence, Eldar unveils the fascinating challenges and breakthroughs in embedding AI into compact devices like microcontrollers and sensors that are rapidly spreading across the globe. This episode promises an exploration into the dynamic realm of TinyML, focusing on its three foundational pillars: real-time analytics, voice, and vision. Eldar discusses the anticipated double-digit growth of this sector over the next decade, making it a pivotal listen for tech enthusiasts and those curious about the future trajectory of AI technology. Eldar also shares how Renesas, through its acquisition of Reality AI, is enhancing its prowess in real-time analytics and tackling high-frequency sampling rates. With a focus on consultancy, data selection, and sensor placement, Eldar illustrates how only a sliver of the TinyML process is devoted to model development. By integrating Reality AI with the E-Square Studio IDE, Renesas is streamlining workflows to maximize the potential of TinyML. Tune in to understand how TinyML is reshaping industries and what lies ahead for Edge AI applications. This episode is packed with insights and strategies for anyone looking to leverage the power of TinyML in their projects. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

EDGE AI POD

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Revolutionizing Education and Healthcare with Cutting-Edge Human-AI Interaction 21:43

26 weeks پیش21:43

21:43

Discover how the fusion of cutting-edge technology and human interaction is revolutionizing education and healthcare with Professor Stella Kuei-An Nguyen from National Yangming Chow Tung University. Join us as we explore her innovative work in Human-AI Interaction using Inertial Measurement Units (IMU), and witness the transition of Raboni IMU devices into the transformative Bony AI. Collaborating with giants like TSMC, MIT, and leading hospitals, Professor Nguyen showcases how these advancements are not only elevating coding education but also offering groundbreaking solutions for medical conditions such as Parkinson’s and dementia. These innovations are powered by tiny machine learning algorithms, seamlessly blending sensors and AI processors into user-friendly interfaces that redefine efficiency in educational and healthcare diagnostics. Delve into the world of advanced motion monitoring technology as we delve into the integration of Rabonit, IMU, and the YSI2 AI processor, designed to intelligently track movements. This sophisticated system promises to revolutionize the way we assess physical activity, especially for children in intermediate playing stages. With a specially designed app that extracts and analyzes movement curves, this technology offers invaluable insights that could enhance both educational environments and healthcare sectors. Join us for an insightful journey into how these tools are set to make a broader impact, aiming to enrich lives by providing effective and efficient information. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

EDGE AI POD

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EDGE AI BLUEPRINTS: Enhancing Urban Safety with Edge AI Approaches to Pedestrian and Traffic Management 1:00:29

27 weeks پیش1:00:29

1:00:29

What do rising pedestrian fatalities and the challenges of implementing intelligent transportation systems have in common? In this episode, we’re joined by Davis Sawyer from NXP and Mike "Witt" Whitaker from the City of Lakewood, Colorado, to unpack the complexities of applying edge AI to enhance urban safety. As Witt recounts his pioneering work with autonomous vehicles since the 1990s, we explore the fast-paced evolution of AI technology and its critical role in addressing real-world problems, such as the increasing need for pedestrian safety and efficient traffic management. We take a hard look at the perplexing trend of fatal crashes involving impaired drivers despite a drop in overall traffic incidents. The conversation intensifies as we discuss the doubling of pedestrian fatalities, particularly at night, and the significant challenges posed by underreported impairment and the rise of fentanyl use. Our guests share insights into how collaborations with the police department and meticulous data analysis can reveal hidden patterns, driving the development of more effective municipal safety strategies. Our exploration doesn’t stop there. We also discuss the advancements in detection technologies and the iterative process of refining systems like LiDAR and video to predict pedestrian movements more accurately. This episode offers a deep dive into the complexities and costs of deploying AI models in urban environments, including the importance of ML Ops, the challenge of model retraining, and the role of existing infrastructure in supporting intelligent systems. Join us as we navigate the intricate world of smart city solutions, focusing on the shared goal of safer streets for everyone. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

EDGE AI POD

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Panel Discussion - EDGE AI TAIPEI - Revolutionizing Edge Computing with AI-Driven Innovations 58:42

28 weeks پیش58:42

58:42

Discover the cutting-edge world of AI deployment on edge devices with insights from top experts, including Dr. KC Liu. This episode promises to unravel the complexities of optimizing AI models for devices where memory and computing power are limited. We explore the critical role of model compilers and the innovative strides being made in AIoT sensors, as Matteo Maravita from STMicroelectronics offers an exciting glimpse into the future of machine learning integration into MEMS sensors. Join us as we tackle the pressing need for standardization in the fragmented IoT development landscape. Our esteemed panel delves into the challenges developers face with diverse proprietary technologies from giants like STM, NXP, and Renesas. Hear about potential convergence through model zoos and frameworks, and the unique role of MLPerf Tiny ML in benchmarking AI applications specifically designed for edge devices. Our conversation shines a light on the balance between utilizing common tools and proprietary compilers for optimized performance on specific hardware. Lastly, explore the promising avenues of AI in the realm of robotics and the innovative strategies shaping the future of AI systems. Learn about the layered architecture approach dividing AI systems into sensor network, edge AI, and cloud computing layers, and the potential for a sustainable AI ecosystem through collaboration. With a focus on benchmarking advancements and MPU design strategies, discover how AI integration with numerous sensors could redefine possibilities in the robotics field. This episode is a compelling journey through the landscape of AI technologies, emphasizing collaboration and innovation for next-generation AI products. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

EDGE AI POD

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Revolutionizing Edge Devices with Energy-Efficient Generative AI Techniques 50:19

29 weeks پیش50:19

50:19

Unlock the secrets of energy-efficient AI as we explore the groundbreaking fusion of Edge AI and generative AI in our latest episode. With expert insights from Victor Jung, a trailblazer in the field, discover how foundational models can be deployed on tiny and embedded systems to revolutionize devices like AR glasses and nanodrones. Listen as we unravel the complexities of deploying neural networks on microcontrollers, with a focus on powerful techniques like quantization, graph lowering, and innovative memory management strategies. Victor guides us through the nuanced process of deploying neural networks, highlighting critical stages like graph lowering and memory allocation. Traverse the intricate front-end and mid-end stages where neural network graphs are optimized, ensuring peak performance on specific hardware platforms. We'll illustrate the importance of efficient memory usage through a fascinating example involving a tiny language model on the Syracuse platform, showcasing the role of quantization and memory management tailored for hardware constraints. Dive into the future of AI deployment on edge devices with a focus on quantization and hardware support. From exploring the potential of foundation models like DenoV2 to discussing the emerging micro scaling format, we uncover the technologies that are making AI more energy-efficient and versatile. Our conversation underscores the importance of viewing memory as a compute asset and the need for ongoing research to enhance system efficiency for generative AI at the edge. Join us for an enlightening episode that highlights the vital steps needed to optimize memory and computing resources for meaningful applications on small platforms. Send us a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org…

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