LETTER FROM THE EDITOR |
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Dear Colleague, The Call for Presentation Proposals for the 2026 Embedded Vision Summit, taking place May 11-13 in Santa Clara, California, is now open! We’re planning more than 100 expert sessions and would love to see your ideas—from physical AI case studies to efficient edge AI techniques to the latest advances in vision language models. Check out the 2026 topics list on the Call for Proposals page for inspiration and to submit your own proposal, due December 5th! Brian Dipert |
DEPTH SENSING AND SLAM OPTIONS |
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Introduction to Depth Sensing: Technologies, Trade-offs and Applications Depth sensing is a crucial technology for many applications, including robotics, automotive safety and biometrics. In this 2025 Embedded Vision Summit Presentation, Chris Sarantos, Independent Consultant with Think Circuits, provides an overview of depth sensing technologies, including stereo vision, time-of-flight (ToF) and LiDAR. He discusses the capabilities, trade-offs and limitations of each technology and explores how these fit with the requirements of typical applications. Sarantos examines key characteristics such as range, depth resolution, transverse resolution, frame rate and eye safety. He also discusses the latest advances in ToF sensors and LiDAR, including the use of single-photon avalanche diode (SPAD) arrays, optical phased arrays and chirped pulse techniques. He presents a comparison matrix summarizing the key characteristics and typical applications of each technology. This comprehensive introduction to depth sensing, including the strengths and weaknesses of each technology, will enable you to make informed decisions for your applications.
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Optimizing Real-time SLAM Performance for Autonomous Robots with GPU Acceleration Optimizing execution time of long-term and large-scale SLAM algorithms is essential for real-time deployments on edge compute platforms. Faster SLAM output means faster map refresh rates and quicker decision-making. RTAB-Map is a popular state-of-the-art SLAM algorithm used in autonomous mobile robots. RTAB-Map is implemented in an open-source library that supports various sensors, including RGB-D cameras, stereo cameras and LiDAR. In this 2025 Embedded Vision Summit talk, Naitik Nakrani, Solution Architect Manager at eInfochips, explains how LiDAR-based SLAM implemented with RTAB-Map can be accelerated by leveraging GPU-based libraries on NVIDIA platforms. He shares a detailed optimization methodology and results. He also shares effective ways in which SLAM algorithms can be accelerated on resource-constrained devices.
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DEVELOPING AND IMPLEMENTING VISION-LANGUAGE MODELS |
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Vision-language Models on the Edge In this 2025 Embedded Vision Summit presentation, Cyril Zakka, Health Lead at Hugging Face, provides an overview of vision-language models (VLMs) and their deployment on edge devices using Hugging Face’s recently released SmolVLM as an example. He examines the training process of VLMs, including data preparation, alignment techniques and optimization methods necessary for embedding visual understanding capabilities within resource-constrained environments. Zakka explains practical evaluation approaches, emphasizing how to benchmark these models beyond accuracy metrics to ensure real-world viability. And to illustrate how these concepts play out in practice, he shares data from recent work implementing SmolVLM in an edge device. |
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LLMs and VLMs for Regulatory Compliance, Quality Control and Safety Applications By using vision-language models (VLMs) or combining large language models (LLMs) with conventional computer vision models, we can create vision systems that are able to interpret policies and enable a much more sophisticated understanding of scenes and human behavior compared with current-generation vision models. In this 2025 Embedded Vision Summit talk, Lazar Trifunovic, Solutions Architect at Camio, illustrates these capabilities with several examples of commercial applications targeting use cases such as ensuring compliance with safety policies and manufacturing regulations. He also shares the lessons his company has learned about the limitations and challenges of utilizing LLMs and VLMs in real-world applications. |
UPCOMING INDUSTRY EVENTS |
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How AI-enabled Microcontrollers Are Expanding Edge AI Opportunities – Yole Group Webinar: November 18, 2025, 9:00 am PT Embedded Vision Summit: May 11-13, 2026, Santa Clara, California |
FEATURED NEWS |
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Synaptics Launches the Next Generation of Astra Multimodal GenAI Processors to Power the Future of the Intelligent IoT Edge FRAMOS Unveils Three Specialized Camera Modules for UAV and Drone Applications Qualcomm to Acquire Arduino, Accelerating Developers’ Access to its Leading Edge Computing and AI STMicroelectronics Introduces New Image Sensors for Industrial Automation, Security and Retail Applications NVIDIA DGX Spark Arrives for World’s AI Developers |
EDGE AI AND VISION PRODUCT OF THE YEAR WINNER SHOWCASE |
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MemryX MX3 M.2 AI Accelerator Module (Best Edge AI Computer or Board) MemryX’s MX3 M.2 AI Accelerator Module is the 2025 Edge AI and Vision Product of the Year Award Winner in the Edge AI Computers and Boards category. The MemryX MX3 M.2 AI Accelerator delivers AI model-based computer vision processing with ultra-low power consumption averaging under 3W for multiple camera applications. The MX3 is based on an advanced on-chip memory architecture that reduces data movement, boosting efficiency and reducing power and cost. 16-bit inference processing delivers high accuracy without the need for retraining or hand-tuning. Model compilation for MX3 is straightforward – the MemryX software stack eases deployment, eliminating the need for deep hardware expertise. Thousands of computer vision models have been directly compiled with no intervention, shortening development cycles and speeding up time-to-market. Developers can import models directly from popular frameworks like TensorFlow or PyTorch, and the MemryX compiler automates optimizations such as quantization and layer fusion. These tools can even run on resource-constrained devices like the Raspberry Pi, enabling cost-effective development and testing. This streamlined workflow eliminates the need for deep hardware expertise, significantly reducing development time and complexity. The MX3 hardware offers an innovative approach to scaling. It allows MX3 devices to be daisy-chained to add capacity for large models while also allowing fewer devices to be deployed to reduce cost and power when performance requirements are lower. The M.2 form factor enables quick integration into existing platforms with minimal thermal concerns. Please see here for more information on MemryX’s MX3 M.2 AI Accelerator Module. The Edge AI and Vision Product of the Year Awards celebrate the innovation of the industry’s leading companies that are developing and enabling the next generation of edge AI and computer vision products. Winning a Product of the Year award recognizes a company’s leadership in edge AI and computer vision as evaluated by independent industry experts. |
