Podcast
EDGE AI POD
Hosted by EDGE AI FOUNDATION · EN
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!
30episodes
Episodes
Newest firstAll episodes
From Fragments to Foundation: The Sound of Progress in Edge Audio AI
Mar 2600:29:18Tap to summarizeWhat if your printer didn’t just spit out pages, but actually understood them? We walk through a hands-on look at multimodal AI on the edge—how visual-language models read layouts, extract tables, translate content, and reformat documents right where data lives, without shipping sensitive files to the cloud. It’s a practical tour from passive peripherals to active intelligence, with real workflows and measurable speedups.We share the architecture behind on-device document intelligence: pre-processing that stabilizes inputs, VLMs that localize and reason over text and images, and post-processing that converts outputs into CSVs, charts, and accessibility-friendly layouts. You’ll hear how Qwen 2.5-VL handles complex visual inputs while maintaining strong language performance, and how a Flux-based diffusion setup enables creative generation and targeted edits—from updating dates in greeting cards to changing borders and colors by prompt. Along the way, we unpack quantization with GGUF to run 7B-class models in tight memory, diffusion sampler and scheduler tuning for latency, and NVIDIA-optimized libraries to squeeze more from modest GPUs.Beyond demos, we dig into business and engineering realities: fine-tuning with enterprise data to reduce hallucinations, building guardrails and fallback paths for reliability, and segmenting large documents to manage VRAM. We also discuss why a companion device—AI PC or smartphone—can orchestrate heavy lifting until printer SOCs catch up, keeping data private and workflows responsive. If you care about document AI, privacy by design, or accessibility features like dynamic type and contrast, this conversation makes the path concrete and actionable.Enjoy the deep dive? Subscribe, share with a colleague who lives in PDFs, and leave a review with the one edge use case you want us to test next.Send us Fan MailSupport the showLearn more about the EDGE AI FOUNDATION - edgeaifoundation.org
Transcribe →Empowering at the Edge: the "Arduino way" to AI
Mar 1900:20:12Tap to summarizeWhat if AI felt like a door you could open, not a wall you had to climb? We dig into how Arduino’s approach—accessibility first, power when you need it—turns the edge AI buzz into a concrete path you can follow, whether you’re a student with a starter kit or an engineer shipping to a fleet.We walk through a practical four-step journey: try AI through no-code experiments, understand it with pre-trained models, train by fine-tuning or starting from scratch with your data, and build something real that lives beyond a demo. Along the way, we unpack a core principle we call “abstraction without obfuscation”—removing friction while keeping the logic transparent—so you can inspect, modify, and truly own the systems you create. That design philosophy shapes everything from our open hardware portfolio (TinyML-friendly MCUs up to Linux-capable MPUs) to our integrations with popular AI frameworks and community-driven libraries.You’ll also hear how cloud-native developer tools streamline the messy middle: browser-based workflows, single-device to fleet deployments, secure OTA updates, data collection for predictive insights, and closed-loop model improvement. Plus, we introduce our AI assistant as a coach that explains code, diagnoses bugs, and helps optimize for memory and speed—turning dead ends into learning moments. Real-world validation from a 35-million-strong community and enterprise teams, including automotive innovators, shows how openness and cohesion accelerate the leap from idea to production.If you care about AI that empowers rather than intimidates, this conversation lays out the playbook. Subscribe, share with a teammate who loves to build, and leave a review telling us the project you’re dreaming about—we might feature it next.Send us Fan MailSupport the showLearn more about the EDGE AI FOUNDATION - edgeaifoundation.org
Transcribe →Faster Edge AI, Fewer Headaches
Mar 1200:59:29Tap to summarizeIf you’ve ever shipped a model that flew in the cloud and crawled on a device, this conversation is a relief valve. We bring on Andreas from Embedl to unpack why edge AI breaks in the real world—unsupported ops, fragile conversion chains, misleading TOPS—and how to fix the loop with a unified, device-first workflow that gets you from trained model to trustworthy, on-device numbers in minutes.We start with the realities teams face across automotive, drones, and robotics: tight latency budgets on tiny chips, firmware that lags new ops, and the pain of picking hardware without reliable performance data. Instead of guesswork, Andreas demos Embedl Hub, a web platform and Python library that standardizes compilation, static quantization, and benchmarking, then runs your models on real hardware through integrated device clouds. The result is data you can act on: average on-device latency, estimated peak memory, compute-unit usage, and detailed, layer-wise latency charts that reveal bottlenecks and fallbacks at a glance.You’ll hear how to assess quantization safely with PSNR (including layer-level drift), why pruning and optimization must be hardware-aware, and how a consistent pipeline across ONNX/TFLite/vendor runtimes tames today’s fragmented toolchains. We also compare Embeddle Hub’s scope to broader end-to-end platforms, touch on non-phone targets available via Qualcomm’s cloud, and talk roadmap: more devices, deeper analytics, and invitations for hardware partners to plug in.If you care about edge AI benchmarking, hardware-aware optimization, ONNX/TFLite compilation, layer-wise profiling, and choosing devices with data instead of hope, you’ll leave with a practical playbook and a tool you can try today—free during beta. Listen, subscribe, and tell us the next device you want to see in the cloud lab. Your model isn’t done until it runs on real hardware.Send us Fan MailSupport the showLearn more about the EDGE AI FOUNDATION - edgeaifoundation.org
Transcribe →TinyML Implementation for a Textile-Integrated Breath Rate Sensor
Mar 800:14:14Tap to summarizeClothes 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 us Fan MailSupport the showLearn more about the EDGE AI FOUNDATION - edgeaifoundation.org
Transcribe →From Lab to Low-Power: Building EMASS, a Tiny AI Chip That Runs on Milliwatts
Mar 401:00:41Tap to summarizeWhat 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 us Fan MailSupport the showLearn more about the EDGE AI FOUNDATION - edgeaifoundation.org
Transcribe →What happens when AI learns from the fire hose—and tests itself on silicon
Feb 2500:59:05Tap to summarizeWhat 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 us Fan MailSupport the showLearn more about the EDGE AI FOUNDATION - edgeaifoundation.org
Transcribe →Survey Data Shows How AI Will Reshape Cars And Why It Belongs On The Edge
Feb 1800:20:42Tap to summarizeWe 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 efficiencySend us Fan MailSupport the showLearn more about the EDGE AI FOUNDATION - edgeaifoundation.org
Transcribe →What happens when you use AI to optimize AI and make AI models run fast anywhere?
Feb 1800:23:31Tap to summarizeTired 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 us Fan MailSupport the showLearn more about the EDGE AI FOUNDATION - edgeaifoundation.org
Transcribe →2026 and Beyond - The Edge AI Transformation
Feb 1100:18:11Tap to summarizeWhat 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 us Fan MailSupport the showLearn more about the EDGE AI FOUNDATION - edgeaifoundation.org
Transcribe →Deploying TinyML Models at Scale: Insights on Monitoring and Automation with Alessandro Grande of Edge Impulse
Oct 13, 2024Tap to summarizeUnlock the secrets of deploying TinyML models in real-world scenarios with Alessandro Grande, Head of Product at Edge Impulse. Curious about how TinyML has evolved since its early days? Alessandro takes us through a journey from his initial demos at Arm to the sophisticated, scalable deployments we see today. Learn why continuous model monitoring is not just important but essential for the reliability and functionality of machine learning applications, especially in large-scale IoT deployments. Alessandro shares actionable insights on how to maintain a continuous lifecycle for ML models to handle unpredictable changes and ensure sustained success.Delve into the intricacies of health-related use cases with a spotlight on the HIFE AI cough monitoring system. Discover best practices for data collection and preparation, including identifying outliers and leveraging Generative AI like ChatGPT 4.0 for efficient data labeling. We also emphasize the importance of building scalable infrastructure for automated ML development. Learn how continuous integration and continuous deployment (CI/CD) pipelines can enhance the lifecycle management of ML models, ensuring security and scalability from day one. This episode is a treasure trove of practical advice for anyone tackling the challenges of deploying ML models in diverse environments.Send us Fan MailSupport the showLearn more about the EDGE AI FOUNDATION - edgeaifoundation.org
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