EDGE AI POD

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

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

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

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

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