EDGE AI POD

EDGE AI FOUNDATION
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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. 13H AGO

    TinyML Implementation for a Textile-Integrated Breath Rate Sensor

    Clothes that quietly listen to your breath might be the missing link between hospital‑grade vigilance and everyday comfort. We walk through how our team built a textile‑integrated breath sensor that actually works in the wild—embroidered interconnects, 3D‑printed dielectric islands, and a carbonized‑silicon yarn strain gauge stitched into a belt—then taught it to estimate breathing at the edge with TinyML. We dig into the engineering choices that matter: why flexible interconnects are the “holy grail” for wearables, how a simple peak detector falls apart with drift and burn‑in, and what it takes to turn raw strain signals into reliable features. After screening public datasets that didn’t match our sensor, we built our own: band‑pass filtering in the 0.1–1 Hz range, three‑second windows, normalization, and event‑button labeling for clean ground truth. From there, we used Edge Impulse’s EON Tuner to search architectures and landed on two contenders—a CNN on time‑domain windows and a compact DNN with wavelet features—then deployed both on an STM32L4 with DMA, timers, and CMSIS‑DSP preprocessing. The results are candid and practical. The CNN was slower but consistently more accurate and robust; the DNN was snappier with lower power but less reliable under offset and noise. Models trained on a different sensor’s data struggled to generalize to our belt, reinforcing a core lesson for smart textiles: sensor‑specific datasets and fine‑tuning are essential. We close by mapping next steps—expanding our dataset, improving transfer across garments and users, exploring hydration prediction, and tightening on‑device optimization—so remote patient monitoring can be seamless, private, and wearable all day. If you enjoy deep dives into edge AI, embedded systems, and human‑centric health tech, follow the show, share it with a colleague, and leave a quick review to help others find it. Send a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

    14 min

  2. 4D AGO

    From Lab to Low-Power: Building EMASS, a Tiny AI Chip That Runs on Milliwatts

    What if the only way to get real gains at the edge is to redesign everything—from the silicon atoms to the app you deploy? That’s the bet Professor-Founder Mohammed Ali made with EMAS, and the results are striking: continuous inference at milliwatts, microsecond wake/sleep cycles, and real benchmarks that hold up against the best in class while burning a fraction of the energy. We walk through how a RISC-V core, dual AI accelerators, and an MRAM/RRAM-backed memory system work together to keep weights on-chip, slash data movement, and power-gate aggressively without losing state. The compiler handles pruning, quantization, and on-the-fly compression to achieve around 1.3 bits per weight without torpedoing accuracy, while a custom memory controller mitigates non-volatile quirks like endurance and read variability. Instead of chasing TOPS, the stack optimizes bandwidth, dataflow, and timing to match the realities of sensors and batteries. The story gets especially interesting with drones. Since propellers—not processors—dominate energy use, EMAS applies tiny AI to the control problem, redistributing load across rotors in real time and extending flight endurance by 60% or more in hardware-in-the-loop simulations. We also dig into wearables and time-series workloads like ECG, audio, and vibration, where sparse sampling pairs perfectly with microsecond power gating. If you build at the edge, the dev experience matters: you’ll hear about the virtual dev kit with remote access to real silicon, a compact evaluation board with modular sensors, and an SDK that plugs into TensorFlow, PyTorch, and Zephyr. Advanced users can map trained models via a CLI; newcomers can lean on a NAS-based flow that proposes architectures meeting strict memory and power budgets. If you care about edge AI, battery life, and shipping reliable products, this conversation is a blueprint for co-designing across the stack to unlock 10–200x energy gains without giving up performance. Subscribe, share with a teammate who owns your edge roadmap, and leave a review with the one use case you’d optimize first. Send a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

    1h 1m

  3. FEB 25

    What happens when AI learns from the fire hose—and tests itself on silicon

    What if your model pipeline started with a simple goal—your dataset, your target chip, and your latency or energy budget—and ended with measured results on real hardware? We sit down with Model Cat CEO Evan Petritis to explore how AI can build on-device AI through a closed loop that’s grounded in silicon, not estimates or hopeful benchmarks. From a live demo to a tour of their “chip farm,” we dig into how the platform searches architectures, tunes hyperparameters, and validates performance using vendor kernels and compilers across MCUs, MPUs, and specialized accelerators. We share the story behind the rebrand from Eta Compute to Model Cat and why the shift matters: AI research moves too fast for traditional, component-by-component toolchains. Evan breaks down five pillars for trustworthy, autonomous model creation—closed-loop goals, reality grounding, system-level intent, modular learning from new research, and a single-step, transparent experience. You’ll hear how teams can upload datasets, get automated analytics on splits and distribution shifts, set constraints like sub–5 ms inference or energy per inference, and see success predictions before training even starts. The demo highlights the silicon library and how each device is profiled in depth—supported ops, kernel speeds, memory footprints—so accuracy, latency, and energy are measured on the actual target. Results come as clear Pareto trade-offs with downloadable artifacts that reproduce on-device. We also field audience questions on exporting to Keras and TFLite, supporting time-series and audio keyword spotting, integrating labeling partners, onboarding new MCUs and accelerators, and the roadmap toward neuromorphic targets and cost estimation. If you care about edge AI, embedded ML, and shipping models that meet real-world constraints, this conversation shows a practical path forward: use AI to navigate the fire hose of research, then prove it on silicon. Enjoy the episode—and if it sparks ideas, subscribe, leave a review, and share it with a teammate who lives in notebooks but dreams in devices. Send a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

    59 min

  4. FEB 18

    Survey Data Shows How AI Will Reshape Cars And Why It Belongs On The Edge

    We share new data showing why drivers see generative AI as a defining force in mobility and how edge inference makes cars faster, safer, and more personal. We map the use cases, hardware shifts, and the move to software-first procurement with clear guidance for builders. • survey highlights on generative AI as a mobility megatrend • definitions and examples of circular economy in vehicles • priority edge use cases in ADAS, safety, and infotainment • hidden value in predictive maintenance and intrusion detection • why inference runs on the edge for latency and reliability • constraints around cost, memory, and over-the-air updates • NPU rise over GPU and evolving CPU roles • software-first buying and model portability trade-offs • smarter sensors, radar AI, and neuromorphic paths • hybrid architectures for sensor fusion and efficiency Send a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

    21 min

  5. FEB 18

    What happens when you use AI to optimize AI and make AI models run fast anywhere?

    Tired of choosing between performance and freedom? We sit down with Stefan Crossin, CEO and co‑founder of YASP, to unpack how a hardware‑aware AI compiler can speed up training, simplify deployment, and finally make model portability real. The story starts with a distributed team in Freiburg and Montreal and moves straight into the heart of the problem: most AI groups burn time on infrastructure and juggle separate stacks for training and inference, all while staying tethered to one dominant vendor’s software ecosystem. Stefan lays out a different path. YASP converts models into a clean intermediate representation, plugs into the tools teams already use, and applies a closed‑loop optimization system that learns the target hardware. Instead of forcing a new language or workflow, a few lines of integration unlock dynamic kernel generation, graph‑level tuning, and one‑click deployment to different chips, clouds, or edge devices. The result is a practical bridge between “write once” ideals and real‑world performance, where being hardware‑aware—not hardware‑bound—delivers speed without lock‑in. We also dive into the market dynamics behind portability. Incumbents protect moats; challengers need bridges. Cloud providers fear shorter runtimes but win when customers get more value per dollar and per watt. With credible benchmarks showing meaningful gains in training and inference, YASP is courting chip makers, CSPs, and end users through a focused beta, a clear roadmap to launch, and a business model that combines free access with subscription tiers. If you’ve been waiting for proof that AI can be both faster and freer across architectures, this conversation makes the case with clarity and detail. Enjoy the episode? Follow the show, share it with a colleague, and leave a quick review—what platform or accelerator would you target first with true portability? Send a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

    24 min

  6. FEB 11

    2026 and Beyond - The Edge AI Transformation

    What if the smartest part of AI isn’t in the cloud at all—but right next to the sensor where data is born? We pull back the curtain on the rapid rise of edge AI and explain why speed, privacy, and resilience are pushing intelligence onto devices themselves. From self‑driving safety and zero‑lag user experiences to battery‑friendly wearables, we map the forces reshaping how AI is built, deployed, and trusted. We start with the hard constraints: latency that breaks real‑time systems, the explosion of data at the edge, and the ethical costs of giant data centers—energy, water, and noise. Then we dive into the hardware leap that makes on‑device inference possible: neural processing units delivering 10–100x efficiency per watt. You’ll hear how a hybrid model emerges, where the cloud handles heavy training and oversight while tiny, optimized models make instant decisions on sensors, cameras, and controllers. Using our BLERP framework—bandwidth, latency, economics, reliability, privacy—we give a clear rubric for deciding when edge AI wins. From there, we walk through the full edge workflow: on‑device pre‑processing and redaction, cloud training with MLOps, aggressive model optimization via quantization and pruning, and robust field inference with confidence thresholds and human‑in‑the‑loop fallbacks. We spotlight the technologies driving the next wave: small language models enabling generative capability on constrained chips, agentic edge systems that act autonomously in warehouses and factories, and neuromorphic, event‑driven designs ideal for always‑on sensing. We also unpack orchestration at scale with Kubernetes variants and the compilers that unlock cross‑chip portability. Across manufacturing, mobility, retail, agriculture, and the public sector, we connect real use cases to BLERP, showing how organizations cut bandwidth, reduce costs, protect privacy, and operate reliably offline. With 2026 flagged as a major inflection point for mainstream edge‑enabled devices and billions of chipsets on the horizon, the opportunity is massive—and so are the security stakes. Join us to understand where AI will live next, how it will run, and what it will take to secure a planet of intelligent endpoints. If this deep dive sparked ideas, subscribe, share with a colleague, and leave a review to help others find the show. Send a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

    18 min

  7. FEB 11

    Edge Computing Revolutionized: MemryX's New AI Accelerator

    Ready to revolutionize your approach to edge AI? Keith Kressin, a veteran with 13 years at Qualcomm before joining MemoryX, shares a breakthrough technology that's transforming how AI operates in resource-constrained environments. MemoryX has developed an architecture that defies conventional wisdom about AI acceleration. Unlike traditional systems dependent on memory buses and controllers, their solution features autonomous parallel cores with localized memory, eliminating bottlenecks and enabling linear scaling from small devices to powerful edge servers. The result? About 20 times better performance per watt than common alternatives like NVIDIA's Jetson platform, all packaged in a simple M.2 form factor that consumes just half a watt to two watts depending on workload. What truly sets MemoryX apart is their software approach. While many AI accelerators require extensive model optimization, MemoryX offers one-click compilation for over 4,000 models without modifications. This accessibility has opened doors across industries – from manufacturing defect detection to construction safety monitoring, medical devices to multi-camera surveillance systems. The technology proves particularly valuable for "brownfield" computing environments where legacy hardware needs AI capabilities without complete system redesigns. The company embodies efficiency at every level. While competitors have raised $250+ million in funding, MemoryX has built their complete hardware and software stack with just $60 million. This resourcefulness extends to their community approach – they offer free software, extensive documentation, and support educational initiatives including robotics camps and hackathons. Curious about bringing AI acceleration to your next project? Visit MemoryX's developer hub for free resources and examples, or purchase their M.2 accelerator directly through Amazon. Whether you're upgrading decades-old industrial equipment or designing cutting-edge multi-camera systems, this plug-and-play solution might be exactly what you need. Send a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

    22 min

  8. FEB 3

    Atym and WASM is revolutionizing edge AI computing for resource-constrained devices.

    Most conversations about edge computing gloss over the enormous challenge of actually deploying and managing software on constrained devices in the field. As Jason Shepherd, Atym's founder, puts it: "I've seen so many architecture diagrams with data lakes and cloud hubs, and then this tiny little box at the bottom labeled 'sensors and gateways' - which means you've never actually done this in the real world, because that stuff is some of the hardest part." Atym tackles this challenge head-on by bringing cloud principles to devices that traditionally could only run firmware. Their revolutionary approach uses WebAssembly to enable containerization on devices with as little as 256 kilobytes of memory - creating solutions thousands of times lighter than Docker containers. Founded in 2023, Atym represents the natural evolution of edge computing. While previous solutions focused on extending cloud capabilities to Linux-based edge servers and gateways, Atym crosses what they call "the Linux barrier" to bring containerization to microcontroller-based devices. This fundamentally changes how embedded systems can be developed and maintained. The impact extends beyond technical elegance. By enabling containers on constrained devices, Adam bridges the skills gap between embedded engineers who understand hardware and firmware, and application developers who work with higher-level languages and AI. A machine learning engineer can now deploy models to microcontrollers without learning embedded C, while the embedded team maintains the core device functionality. This capability becomes increasingly crucial as edge AI proliferates and cybersecurity regulations tighten. Devices that once performed simple functions now need to run sophisticated intelligence that may come from third parties and require frequent updates - a scenario traditional firmware development approaches cannot efficiently support. Ready to revolutionize how you manage your edge devices? Explore how Atym's lightweight containerization could transform your edge deployment strategy. Send a text Support the show Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org

    25 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
    85
  • Rating
    Clean
  • Copyright
    © 2026 EDGE AI FOUNDATION
  • Show Website
    EDGE AI POD

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