EDGE AI POD

EDGE AI FOUNDATION
  • 4.0 (2)
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Discover the cutting-edge world of energy-efficient machine learning, edge AI, hardware accelerators, software algorithms, and real-world use cases with this podcast feed from all things in the world's largest EDGE AI community.  These are shows like EDGE AI Talks, EDGE AI Blueprints as well as EDGE AI FOUNDATION event talks on a range of research, product and business topics. Join us to stay informed and inspired!

  1. 2d ago

    Village OS: AI For Sustainable Living

    What if a neighborhood could think, heal, and feed itself? We sit down with James Ehrlich of Stanford to unpack Village OS, a generative AI platform that designs resilient communities by starting with a simple question: what does the land want? From the urban edge of Riyadh to peri-urban sites worldwide, James shows how geospatial data, climate histories, hydrology, and cultural patterns come together to shape housing, farms, energy, and mobility as one living system. We trace James’s path from early game design and digital effects into the world of eco-villages and permaculture, where taste, health, and connection inspired a research agenda: use technology to serve nature and people. The demo moves from contour maps and fluid dynamics to soil restoration, aquaponics, and agrovoltaics that grow shade crops under solar. Real-time modeling toggles apartments, townhomes, and single-family mixes while projecting costs, returns, and service loads for water, energy, and waste. The punchline is elegant: at the neighborhood scale, waste becomes an asset, powering heat, cooling, and purification while closing loops for food and energy security. Funding and measurement get equal attention. Village OS projects ESG and SDG outcomes and carbon sequestration across decades, offering a transparent view for sovereign wealth funds, pensions, and institutional capital. After groundbreak, the operating layer shifts to edge AI: tinyML sensors and small language models form a digital mycelial network with low latency, low energy, and high autonomy, connected by a thin, privacy-safe cloud channel for cross-site learning. It’s resilience defined by human well-being—lower stress, safer streets, access to fresh food, and spaces for elders and children—backed by systems that can ride out disruption. If you care about sustainable housing, regenerative agriculture, microgrids, and the future of edge AI, this conversation offers a practical, hopeful blueprint. Subscribe, share with a friend who’s into systems thinking, and leave a review with the one feature you’d want in your ideal resilient neighborhood. Send us Fan Mail Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

    1h 1m

  2. Jun 4

    When Edge AI Meets Hearing Loss, Access Gets Real

    Crowded cafés, clinking plates, and echoey halls make conversations exhausting. We set out to change that by fitting real deep learning into an ear-sized device and proving it can separate speech from noise with almost no delay or battery hit. The result isn’t louder sound; it’s clearer lives and less fatigue. We walk through the full Clara enhancement path: transforming raw mic input into log-mel features, stabilizing for gain shifts, and feeding a 40-layer temporal convolutional recurrent network that predicts a mask to preserve voice and suppress noise. Then we show how a light touch of the original signal brings back space and warmth, avoiding the hollow, underwater audio that turns people off. Along the way, we tackle painful transients—the cutlery and clatter that spike hearing aids—and explain how wide dynamic range compression keeps everything comfortable and intelligible. The heart of the story is edge AI done right. Our SPU001 chip uses unstructured sparsity to skip zero multiplies in hardware, shrinking memory needs and power draw by orders of magnitude. That lets a pruned model with effective 10 MB scale run from just one MB of SRAM while holding algorithmic latency near eight milliseconds and total path time under ten. Metrics back it up: higher scale-invariant signal-to-distortion ratios, better hearing aid speech quality scores, and strong user reports. A rapid partnership with New Sound brought this to market in about three months, and audiologists on a noisy show floor heard the difference immediately. If you care about hearing tech, edge computing, or just making conversations effortless again, this one is for you. Hear how small silicon and smart modeling turn “AI” from a buzzword into a daily benefit. Subscribe for more deep dives on practical edge AI, share with someone who struggles in noisy rooms, and leave a review with your toughest audio environment—we might feature it next. Send us Fan Mail Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

    15 min

  3. May 28

    Cows Chewed Our Sensors And Still Taught Us About Edge AI

    A failed 5G rollout in a legendary forest forced us to rethink everything we knew about AI infrastructure. Instead of pushing data to distant servers, we turned wearables, sensors, and tiny controllers into a cooperative network that can sense, decide, and act without the cloud. The result is a hands-on tour of decentralized AI: how to split models across devices, why feature fusion matters more than raw horsepower, and what it takes to make ad hoc networks reliable in the wild. We walk through practical patterns for collaboration at the edge, from complementary sensing in search-and-rescue to pooled compute in crowded venues. You’ll hear how we orchestrate parallel processing on microcontrollers, assign inference to one core and radio handling to another, and compress features to keep bandwidth low. We also dig into continual learning and federated averaging, outlining strategies to adapt models locally while protecting privacy and avoiding catastrophic forgetting. Along the way, we share early results from agriculture and public safety pilots, plus the gritty realities of hardware constraints, scarce datasets, and the challenge of testing at scale. If you’re curious about TinyML, edge AI, and how generative models might run collaboratively across many small devices, this conversation lays out a practical path forward. You’ll come away with a clearer picture of when decentralization beats centralized cloud systems, which protocols survive in noisy environments, and why the future of AI may look less like a monolith and more like a swarm. Subscribe, share this episode with a builder who loves constraints, and leave a review to tell us where you’d deploy a swarm of tiny models next. Send us Fan Mail Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

    22 min

  4. May 21

    How AI Compensates for PID Controller Limitations in Electric Vehicles with STMicroelectronics

    How can artificial intelligence transform electric vehicle performance? Discover the groundbreaking application of neural networks to motor control challenges that even Formula 1 legend Michael Schumacher helped identify. The automotive industry's electrification demands increasingly sophisticated silicon solutions, particularly for traction inverters controlling electric motors. Traditional control systems face a fundamental challenge: they must operate at extraordinary speeds (currently 20kHz, trending toward 100kHz) while managing rapid transitions between states. When drivers make sudden accelerator changes, conventional PID controllers produce energy-wasting overshoots that drain precious battery power. Our research presents a novel approach using neural networks to compensate for these limitations. By generating time-varying correction factors, our AI solution reduces maximum overshoots by up to 70% in demanding scenarios. This innovation represents a critical advancement for electric vehicle efficiency, potentially extending range and improving performance. What makes this application particularly fascinating is the extreme time constraints. While most AI applications process data at relatively leisurely rates (think 30 frames per second for vision systems), motor controllers must complete their calculations within microseconds. Our current implementation achieves 70-microsecond inference times on automotive-grade microcontrollers, with further optimizations planned through hardware acceleration. The collaboration between academic researchers and industry partners (MathWorks and STMicroelectronics) demonstrates the power of combining simulation expertise with real-world deployment capabilities. Using Simulink as the development platform and ST's developer cloud for automatic deployment to physical microcontrollers, we've created a streamlined methodology for applying AI to automotive control systems. Want to dive deeper into the technical details? Check out our published research paper on arXiv and discover how neural networks are transforming the heart of electric vehicle propulsion systems. Share your thoughts on how AI might further revolutionize automotive technology! Send us Fan Mail Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

    17 min

  5. May 14

    How to simplify and securely maintain up-to-date AI Models in the Edge

    Ever shipped a smart device and worried what happens after it leaves the lab? We dig into the hard parts of edge security—where models live on-device, firmware updates are routine, and attackers treat your fleet as a supply chain—then break them down into moves any team can adopt. From secure boot that blocks untrusted code at power-on to verified boot with discrete secure elements, we show how to anchor trust in hardware so software can prove itself before it runs. We walk through the real risks teams face—model theft, OTA hijacking, plaintext credentials in flash, and silent downgrades—and map them to practices that actually scale across mixed hardware. You’ll hear why encrypting data at rest frustrates drive cloning, how end-to-end encrypted and signed updates prevent tampering, and why automatic rollback turns “bricks” into recoverable hiccups. Updating AI models becomes a strength when you ship small, signed artifacts instead of full images, with logs that satisfy CRA and NIS2 audits while giving operators the visibility they need. We also tackle the build-versus-buy dilemma with clear-eyed math. Building a secure update stack across Qualcomm, NXP, PSoC, and diverse compute modules takes specialists and months; a platform approach spreads cost, speeds delivery, and still lets you own your keys so you can switch later without stranding devices. That key ownership underpins true end-to-end trust: you sign, devices verify, and the infrastructure moves at your pace. If you care about safeguarding IP, maintaining uptime, and earning customer trust, this is your blueprint. If this deep dive helps, follow the show, share it with your hardware and firmware teams, and leave a quick review—what part of your edge stack needs the strongest lock? Send us Fan Mail Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

    21 min

  6. May 7

    AI-Driven Brain-Computer Interface (BCI) Unlocking the Minds Potential

    Imagine steering a game or selecting a letter with nothing but a blink or a glance. We set out to make that feel normal, not magical, by building a non-invasive brain–computer interface that runs entirely on a low-power microcontroller and fits into everyday wearables like glasses. No surgery, no cloud dependency—just smart sensing, tight signal processing, and a tiny neural net that turns eye movements into reliable commands. We start with the “why”: millions live with motor impairments yet can still move their eyes, leaving a powerful window for communication and control. From there, we map the BCI landscape—high-precision invasive implants like Neuralink, BrainGate, and Synchron on one side; accessible non-invasive tools like Emotiv, Muse, and OpenBCI on the other—and unpack the trade-offs across accuracy, latency, cost, and ethics. Our approach uses electrostatic charge sensing to read subtle changes around the eyes, with electrodes positioned for comfort and signal quality. A lean pipeline cleans the data with high-pass, notch, and low-pass filters; a Z-score event detector wakes the model only when something meaningful happens. The model is a compact 1D CNN that classifies four classes—discard involuntary blinks, trigger with a voluntary blink, and detect left or right glances—achieving about 90% accuracy on a small multi-participant dataset. Running on an STM32H7, it uses roughly 18 KB flash and 6 KB RAM, with sub-millisecond inference; the overall response is driven by the short data window at 240 Hz, delivering real-time control for basic tasks. We demo blink-to-jump and look-to-steer gameplay to prove responsiveness and highlight how the same system could power communication aids and smart-home control. Looking ahead, we focus on integrating the electrodes into comfortable glasses, adding quick calibration for personal variability, and expanding the command set without sacrificing simplicity. If this mix of accessibility, edge AI, and practical human–machine interaction resonates with you, follow the show, share it with a friend, and leave a review so we can reach more builders and caregivers working on assistive tech. What would you control first with a glance? Send us Fan Mail Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

    15 min

  7. Apr 30

    An Embedded Transformer- base face recognition system in the STM32N6

    What if transformer-level face recognition could run on a microcontroller without giving up speed or accuracy? We set out to make that real on the STM32N6 by pairing its neural processing unit with a hybrid model that blends convolutional efficiency and attention-like global context. Along the way, we rewired core assumptions about attention, reworked unsupported operators, and delivered a full on-device pipeline that actually feels instant. We start with the hardware edge: ARM Cortex M55, 4 MB of continuous RAM, and an NPU pushing up to 600 GOPS at remarkable power efficiency. That lets us chain models—RetinaFace-style detection with landmarks, alignment for a stable canonical view, MobileNetV2 anti-spoofing to block print and replay attacks, and a final recognizer that outputs a 512‑dimensional embedding. The recognizer is built on EdgeFace, itself based on EdgeNext, chosen for its sweet spot between parameter count and accuracy. It behaves like a transformer where it matters—capturing long-range relationships—yet fits into the tight compute envelope of a microcontroller. The turning point is attention without the dot product. Because the ST toolchain doesn’t support batch matmul, we replaced it with a convolutional self-attention mechanism. Depthwise and pointwise convolutions encode relationships across pixels and channels, a sigmoid stands in for softmax, and element-wise products reconstruct attention’s weighting behavior. This maps cleanly to the NPU, avoids quadratic costs, and preserves the ability to stabilize identities across pose, lighting, and occlusion. Benchmarks show roughly 40 ms per frame end to end—about 25 FPS—plus substantial speedups over STM32H7 and higher accuracy than MobileFaceNet across validation sets. That opens doors for privacy-first access control, frictionless enrollment on-device, and personalized experiences where latency matters and data should never leave the edge. If you’re exploring embedded AI, this walkthrough shows how to align model design with silicon capabilities and deliver results that feel both fast and trustworthy. Enjoy the deep dive? Subscribe, share this episode with a fellow edge AI builder, and leave a quick review to help others find the show. Send us Fan Mail Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

    12 min

  8. Apr 23

    Verification, Validation & Certification of AI in Safety-Critical Applications

    A cyclist disappears to the model, not to your eyes—and that mismatch is the heart of safety-critical AI. We open with the “vanishing cyclist” to show how tiny, imperceptible perturbations can flip life-or-death decisions, then walk through a practical path to trust that spans data, verification, and deployment. Along the way, we share real stories from BMW, Airbus, and Madrid Metro to ground the engineering in results, not hype. We break down how to build a resilient pipeline: domain-specific data labeling, realistic synthetic generation for rare and risky scenarios, and tight interoperability across MATLAB, Python, PyTorch, TensorFlow, and ONNX. We dig into explainability beyond classification with D-RISE for object detectors and semantic segmentation, helping you see what the network actually uses to decide. Then we raise the bar with formal verification for robustness—mathematical guarantees within defined perturbation sets—so you aren’t mistaking the absence of found attacks for true safety. Finally, we get practical about the edge. Model compression and projection recover accuracy with fewer parameters, enabling fast, power-efficient deployment to CPUs, GPUs, and FPGAs, backed by code generation for the entire application. We also cover runtime safeguards like out-of-distribution detection to catch smog-on-the-runway moments and escalate safely. Throughout, we connect the work to evolving standards, the EU AI Act, and updated workflows that adapt the V-model for learning systems, so your process and artifacts are ready for audits and certification. If you care about trustworthy AI for cars, planes, rail, and medical devices—and want tools and habits that survive contact with reality—this one’s for you. Listen, subscribe, and leave a review with your biggest trust gap or the safeguard you’d ship first. Send us Fan Mail Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

    19 min
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About

Discover the cutting-edge world of energy-efficient machine learning, edge AI, hardware accelerators, software algorithms, and real-world use cases with this podcast feed from all things in the world's largest EDGE AI community.  These are shows like EDGE AI Talks, EDGE AI Blueprints as well as EDGE AI FOUNDATION event talks on a range of research, product and business topics. Join us to stay informed and inspired!

Information

  • Creator
    EDGE AI FOUNDATION
  • Years Active
    2024 - 2026
  • Episodes
    99
  • Rating
    Clean
  • Copyright
    © 2026 EDGE AI FOUNDATION
  • Show Website
    EDGE AI POD

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