Videos on Edge AI and Visual Intelligence
We hope that the compelling AI and visual intelligence case studies that follow will both entertain and inspire you, and that you’ll regularly revisit this page as new material is added. For more, monitor the News page, where you’ll frequently find video content embedded within the daily writeups.
Alliance Website Videos
Microchip’s Demonstration of an SDI-to-HDMI® Cross-Converter on PolarFire FPGAs
Prakash Battu, Senior Manager of Design Engineering at Microchip, shares the company’s latest SDI-to-HDMI cross-converter solution. Built on PolarFire® FPGAs, the application supports dynamic rate switching and embedded audio across all SDI standards from 1.5G to 12G, and a standard HDMI IP for seamless interoperability. Because the design that delivers 2× better power efficiency, it
Real-Time Vision-Language Inference on AMD Radeon™ iGPU Using ROCm™
This demonstration showcases a real-time vision-language inference pipeline running on an AMD Radeon™ integrated GPU, highlighting multimodal AI capabilities on power-efficient embedded platforms. The system processes live or recorded video streams and enables interactive question answering based on visual scene understanding. A lightweight Vision-Language Model (VLM) is deployed to jointly interpret visual inputs and natural
AMD Versal™ AI Edge Series Gen 2Hard ISP Featuring 12x Sensor Inputs
This demo showcases the AMD Versal™ AI Edge Series Gen 2 VEK385 evaluation board’s ability to support streaming raw video data from 12 different CMOS image sensors concurrently and process 14 live streams out through the hardened ISP blocks in a single 2VE3858 adaptive SoC. It features a mix of sensors with different resolutions and
AI Agents on AMD – Secure Agent Computing at the Edge
See how agentic AI workflows can use both local AMD hardware and cloud resources to improve privacy, performance, and cost efficiency. This demo showcases AI agents running with the AMD ROCm™ software platform. Private and data-sensitive tasks can remain on the local system, while more demanding workloads can be sent to cloud-based models when needed.
The Eyes of Physical AI: Market Opportunities for MagikEye’s ILT Technology
Feisal Afzal discusses the growing market opportunities for MagikEye’s Invertible Light™ Technology across robotics, smart appliances, automotive systems, and emerging Physical AI platforms. The conversation explores how efficient depth perception can enable safer, smarter, and more capable machines operating at the edge. With increasing demand for spatial intelligence in the physical world, ILT is positioned
Introducing ILT Pico: Compact Depth Sensing for Physical AI
Jan Heller showcases ILT Pico, MagikEye’s compact developer platform built around its patented Invertible Light™ Technology. The system delivers precise depth perception with low power consumption and reduced compute requirements compared to conventional approaches. Designed for robotics, smart appliances, and edge AI applications, ILT Pico makes advanced machine vision more accessible to developers and innovators.
MagikEye ILT Enables Real-Time Robot Navigation and Obstacle Avoidance
Watch a mobile robot powered by MagikEye’s Invertible Light™ Technology (ILT) navigate autonomously while detecting and avoiding obstacles in real time. This demonstration highlights how ILT delivers low-latency depth perception using a compact, power-efficient sensor architecture. The technology is designed for robotics, automation, and Physical AI applications where reliable spatial awareness is essential. Learn how
Smart Sensor Demo: On-Device Object Detection with Lattice CertusPro™-NX
Lattice Semiconductor demonstrates how the CertusPro-NX FPGA bridges an image sensor to a Raspberry Pi, performing on-device pre-processing and object detection before passing data to the host CPU. Sensor frames at 30 fps are fed into the FPGA, where an object detection model — trained on eight automotive object classes — runs locally and outputs
“From YOLO to SAM: Segmentation Models on Real Edge Hardware,” a Presentation from Au-Zone Technologies
Sébastien Taylor, VP of R & D at Au-Zone Technologies presents “From YOLO to SAM: Segmentation Models on Real Edge Hardware” at the May 2026 Embedded Vision Summit. Segmentation is fundamental to edge vision—from drivable surface detection to industrial inspection. But how do different approaches actually perform on resource-constrained hardware?… “From YOLO to SAM: Segmentation
Lattice FPGA Embedded Vision: Real-Time ISP Pipeline with Integrated Tuning Tool
Lattice Semiconductor demonstrates a complete end-to-end vision pipeline running entirely on an FPGA, from CSI camera input to screen output. Raw sensor data is processed by an on-chip ISP that converts it to a clean YUV 422 video stream in real time. The platform includes a dedicated ISP builder and tuning tool, allowing designers to
