Amir Sherman, Head of Global Business Development at Peridio presents “From Chips to Platforms: Scaling Edge AI with SoMs, Production Linux and Secure Life-Cycle Ops” at the May 2026 Embedded Vision Summit. Edge AI has moved beyond choosing a single chip or platform. Teams now face a harder question: how… “From Chips to Platforms: Scaling […]
Pete Warden, CEO at Moonshine AI presents “Building a Local Voice Agent on a Raspberry Pi” at the May 2026 Embedded Vision Summit. In this talk, Warden explains everything you need to know to build your own voice agent running entirely on a stock Raspberry Pi 5, with no internet… “Building a Local Voice Agent
“Building a Local Voice Agent on a Raspberry Pi,” a Presentation from Moonshine AI Read More +
Mike Leonard, Software Architect at Quadric presents “Porting and Optimizing Advanced Vision-Language-Action Models for Embedded Autonomous Systems” at the May 2026 Embedded Vision Summit. World-scale vision-language-action (VLA) models are the new frontier in AI for autonomous driving and robotics, enabling systems to perceive, reason and act in complex real-world environments.… “Porting and Optimizing Advanced Vision-Language-Action
Kwabena Agyeman, Joseph Nelson, President at OpenMV presents “How to Train an AI Model Using Roboflow and Deploy to an MCU with an NPU” at the May 2026 Embedded Vision Summit. Running computer vision on microcontrollers is becoming practical thanks to MCUs that integrate NPUs, enabling low-latency inference with tight… “How to Train an AI
Frantz Lohier, Satya Mallick, Senior WW Specialist—Advanced Computing, AI and Robotics at Amazon Web Services presents “COOL: Accelerating Computer Vision Workloads with Cloud Optimized OpenCV on AWS” at the May 2026 Embedded Vision Summit. As computer vision workloads grow in scale and complexity, developers need high-performance, cost-efficient infrastructure to keep… “COOL: Accelerating Computer Vision Workloads
Mayank Mangla, AI Product Manager and Systems Architect at MIPS, a GlobalFoundries company presents “No RISC, No Reward: Unlocking Extreme Efficiency in Physical AI with RISC-V” at the May 2026 Embedded Vision Summit. Deployment of neural networks at the edge is often constrained by the rigidity and integration cost of… “No RISC, No Reward: Unlocking
Petronel Bigioi, CEO at FotoNation presents “Always-On Edge Perception Via a Heterogeneous Near-Memory AI Architecture” at the May 2026 Embedded Vision Summit. Always-on perception is becoming a defining capability of next-generation edge devices, from AR glasses and hearables to battery-operated sensors. Yet continuous audio/video and motion understanding runs into two… “Always-On Edge Perception Via a
Athish Rahul Rao, Staff Software Engineer at Expedera presents “From Compute-Bound to Memory-Bound: Edge AI Architectures for VLMs” at the May 2026 Embedded Vision Summit. Today’s edge AI hardware was built for CNNs, but vision language models (VLMs) have completely different bottlenecks—especially in safety-critical, latency-sensitive applications like in-cabin automotive intelligence.… “From Compute-Bound to Memory-Bound: Edge
Barrie Mullins, Assistant Vice President at eInfochips (an Arrow company) presents “Navigating Physical AI Deployment Across Multiple Platforms for Automated Optical Inspection” at the May 2026 Embedded Vision Summit. As automated optical inspection moves from the server room to the factory floor, the promise of “seamless” AI deployment often hits… “Navigating Physical AI Deployment Across
Shang-Hung Lin, Vice President of NPU Technology at VeriSilicon presents “One Silicon, Two Worlds: NPU Optimization for Autoregressive and Diffusion Transformers” at the May 2026 Embedded Vision Summit. Physical AI is caught between two computational titans: autoregressive (AR) transformers, which predict discrete action tokens, and diffusion transformers (DiTs), which refine… “One Silicon, Two Worlds: NPU
Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.
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