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
“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
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
“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
Synetic Demonstration of Synthetic Training Data for Computer Vision Edge Case Coverage
David Scott, CEO & Founder at Synetic, makes the case for synthetic training data as the most practical solution to edge case coverage in computer vision at the 2025 Embedded Vision Summit. Real-world data collection
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
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
Synetic Demonstration of the LYNX SDK Beta: Real-Time CV Across Six Platforms in One Call
David Scott, CEO & Founder at Synetic, demonstrates the LYNX SDK beta release at the 2026 Embedded Vision Summit. LYNX returns detection, segmentation, depth, and keypoints simultaneously from a single forward pass, eliminating the need
Synetic Demonstration of the LYNX Computer Vision SDK and Synthetic Data as a Service Platform
Will Ruffalo, Co-Founder at Synetic, introduces two of the company’s core offerings at the 2026 Embedded Vision Summit: the LYNX computer vision SDK and Synetic’s synthetic data as a service platform. LYNX delivers detection, segmentation,
What Quality Managers Get Wrong Before a Single Camera is Installed
This blog post was originally published at Lincode’s website. It is reprinted here with the permission of Lincode. The pilot looked great. Detection rates were strong, the demo impressed leadership, and the vendor was confident. Then
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
