Resources
In-depth information about the edge AI and vision applications, technologies, products, markets and trends.
The content in this section of the website comes from Edge AI and Vision Alliance members and other industry luminaries.
All Resources
Avocado OS x Grinn: Rapid Physical AI Development on Edge Vision Hardware
At EVS 2026, Bill Brock, CEO of Peridio, joins Robert Atrea, CEO of Grinn Global, to announce a co-development partnership bringing Avocado OS to Grinn’s AstroSOM 1680 platform. Together, they demonstrate how Avocado OS integrates
The Arduino UNO Q Board: Unpack the Dual-Brain Power for Next-Gen Edge AI
This blog post was originally published at Qualcomm’s website. It is reprinted here with the permission of Qualcomm. Get ready to reimagine what’s possible with Arduino! The new Arduino® UNO Q is shaking things up by bringing a
BrainChip Partners with Celus to Bring Its Neuromorphic Edge AI Processor to the CELUS Design Platform
Strategic collaboration will soon give engineers direct access to BrainChip’s AKD1500 architecture through CELUS’s AI-guided design tools for device design LAGUNA HILLS, Calif. and MUNICH — July 23, 2026 — BrainChip Holdings Ltd. (ASX: BRN, OTCQX: BRCHF,
Avocado Connect: From Dev Kit to Production for Physical AI Fleets (with Advantech)
In this video, Justin Schneck, CTO and Co-Founder of Peridio, introduces Avocado Connect, the fleet management platform for deploying and operating Avocado OS in production physical AI environments. He explains the gap between building on
Device Infrastructure for Physical AI: Introducing Avocado OS
Justin Schneck, CTO and Co-Founder of Peridio, introduces Avocado OS, the company’s modern embedded Linux operating system designed for teams building physical AI devices and products. In this video, Schneck explains how the rise of
How Robotic Color Sorting Works: Vision, Classification, and Motion Control from Camera to Gripper.
This blog post was originally published at Geisel Software’s website. It is reprinted here with the permission of Geisel Software. Sorting apples by color sounds simple enough. In fact, it is the kind of project
Nota AI Demonstration of LLM Acceleration on Mobilint NPU, Optimized with NetsPresso
Thibault Castells, Research Engineer at Nota AI, demonstrates the company’s latest edge AI technologies at the 2026 Embedded Vision Summit. Specifically, he demonstrates Qwen3-4B running as a real-time LLM Coding Assistant entirely on Mobilint MLA100
Nota AI Demonstration of VLA Robotics on Dragonwing, Enhanced by NetsPresso
Thibault Castells, Research Engineer at Nota AI, demonstrates the company’s latest edge AI and vision technologies at the 2026 Embedded Vision Summit. Specifically, he demonstrates VLA optimization on Qualcomm Dragonwing IQ-9075, powered by NetsPresso. Using
NCore: An Open-Source Multi-Sensor Data Platform for Neural 3D Reconstruction and Physical AI
NCore’s interactive 3D viewer showing camera frustums with images, lidar point clouds, 3D cuboid bounding boxes, and rig trajectory from an autonomous vehicle sequence. This blog post was originally published at NVIDIA’s website. It is
Technologies
“Vision-Language Models in Practice: Architecture and Performance,” a Presentation from AMD
Rajy Rawther, PMTS Software Architect at AMD presents “Vision-Language Models in Practice: Architecture and Performance” at the May 2026 Embedded Vision Summit. Unimodal vision systems are powerful but limited when users need flexible queries, richer semantics or reasoning that combines images/video with language. Vision-language models (VLMs) address this gap by… “Vision-Language Models in Practice: Architecture
What are the Key Engineering Challenges in Autonomous Docking Systems and How are they Solved? (Part 2)
This blog post was originally published at e-con Systems’ website. It is reprinted here with the permission of e-con Systems. Designing an ROS 2-based autonomous docking system is only half the story. Deploying it in the real world is another matter entirely. Since real industrial environments don’t cooperate with textbook assumptions, understanding these challenges will help
“Small Language Models for Edge AI: Trade-Offs and Quantization in Practice,” a Presentation from AMD
Dwith Chenna, MTS Product Engineer, AI Inference at AMD presents “Small Language Models for Edge AI: Trade-Offs and Quantization in Practice” at the May 2026 Embedded Vision Summit. Large language models are powerful but often impractical for embedded and on-prem systems due to latency, cost, privacy and memory constraints. Small… “Small Language Models for Edge
Applications
What are the Key Engineering Challenges in Autonomous Docking Systems and How are they Solved? (Part 2)
This blog post was originally published at e-con Systems’ website. It is reprinted here with the permission of e-con Systems. Designing an ROS 2-based autonomous docking system is only half the story. Deploying it in the real world is another matter entirely. Since real industrial environments don’t cooperate with textbook assumptions, understanding these challenges will help
Seeing the Invisible: How Advanced AFEs Give Robots True Self Awareness
This blog post was originally published at NXP Semiconductors’ website. It is reprinted here with the permission of NXP Semiconductors. If cameras, LiDAR and IMUs are a robot’s eyes and inner ear, then precision analog measurement is its “sense of self”, the quiet awareness of currents, strains and temperatures that tell you how the machine
Yole Group Forecasts Automotive Semiconductor Market Will Double to $160 Billion by 2031
Yole Group has released Automotive Semiconductor Trends 2026, a new market report examining how electrification, advanced driver-assistance systems, centralized computing and software-defined vehicle architectures are reshaping the automotive semiconductor industry. Yole forecasts that the market will more than double from $77 billion in 2025 to $160 billion in 2031, representing a compound annual growth rate
Functions
BrainChip Demonstration of the Company’s Always-On Radar-Based Edge Classification
Ritik Shrivastava, Solutions Architect at BrainChip, demonstrates Akida micro-doppler radar classification, a powerful radar-based object classification solution that runs entirely on-device without internet connection. Shrivastava showcases a live demo using BrainChip’s AKD1500 card attached to a Raspberry Pi, where the system detects and classifies between a bird and drone target in real time using radar.
AI On: 3 Ways to Bring Agentic AI to Computer Vision Applications
This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Learn how to integrate vision language models into video analytics applications, from AI-powered search to fully automated video analysis. Today’s computer vision systems excel at identifying what happens in physical spaces and processes, but lack the abilities to explain the
SAM3: A New Era for Open‑Vocabulary Segmentation and Edge AI
Quality training data – especially segmented visual data – is a cornerstone of building robust vision models. Meta’s recently announced Segment Anything Model 3 (SAM3) arrives as a potential game-changer in this domain. SAM3 is a unified model that can detect, segment, and even track objects in images and videos using both text and visual
