Meetup In 10 days
November
Friday
18:00–21:00 2026 · Tokyo time
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Description How do you make AI respond quickly when compute, memory and power are limited? Join Tokyo AI (TAI) for a technical evening exploring the systems work behind efficient inference, from GPU profiling to reliable embedded applications and agent-assisted deployment. Designed for ML engineers, systems developers and engineers building edge AI products, the session will connect low-level implementation choices with the latency and reliability people experience in real applications. We are planning three technical talks covering: Engineering reliable real-time computer vision applications on embedded GPUs, from model deployment to field operations. Using AI agents to coordinate hardware-aware optimization, reuse deployment knowledge and validate results. Finding inference bottlenecks through GPU profiling and low-level code. When: Friday, November 13, 2026, 18:00-21:00 JST. The date remains a placeholder; talk order may be adjusted. Where: Tokyo, venue to be announced. Agenda Provisional schedule. Each 30-minute talk includes Q&A. Additional speakers and final talk titles will be announced once confirmed. 18:00 - Doors open 18:25-18:30 - Welcome 18:30-19:00 - Talk 1: From Model to Field: Engineering Reliable Edge AI Applications on Embedded GPUs (Antoine Sauvage) 19:00-19:30 - Talk 2: AI Agents for Efficient Edge Deployment (Xiao Wang) 19:30-20:00 - Talk 3: GPU profiling and inference bottlenecks (speaker to be announced) 20:00-21:00 - Networking 21:00 - Doors close Talks Antoine Sauvage and Xiao Wang's talk details are below. The third talk remains a proposed direction; its speaker and final abstract will be announced once confirmed. Talk 1: From Model to Field: Engineering Reliable Edge AI Applications on Embedded GPUs Speaker: Antoine Sauvage, Deputy Director of Engineering, EDGEMATRIX Abstract: Getting an AI model running on an embedded accelerator is only one part of building a production Edge AI application. In the field, inference has to coexist with video ingestion, application logic, limited compute and memory, device management, monitoring, and failure recovery. In this talk, I will share practical lessons from developing real-time computer vision systems on NVIDIA Jetson platforms. I will look at the engineering path from deploying an optimized model to operating a complete application: integrating GPU-accelerated video pipelines, working within embedded hardware constraints, handling unreliable cameras and networks, and designing systems that can be maintained and updated over time. The focus will be on the gap between getting an AI demo running and building an Edge AI application that can operate reliably in the real world. Bio: Antoine Sauvage is Deputy Director of Engineering at EDGEMATRIX in Tokyo, where he works on the architecture and development of Edge AI platforms and applications. He has over 20 years of experience across industrial systems, IoT, cloud infrastructure, and edge computing. His current work focuses on real-time computer vision and the deployment of AI applications on NVIDIA Jetson platforms. Talk 2: AI Agents for Efficient Edge Deployment Speaker: Xiao Wang, Senior Director, Renesas Electronics Abstract: What if deploying AI on an edge device could become a learning process, with each deployment making the next one easier? This talk explores how AI agents can turn deployment goals into coordinated action, drawing on specialized expertise and memory to navigate hardware constraints. We’ll examine how to build frameworks that learn from experiments, reuse knowledge, and balance autonomous exploration with reliable validation. Attendees will gain a perspective on where agents can reduce engineering effort and how to evaluate their impact on deployment quality and efficiency. Bio: Xiao Wang is a Senior Director at Renesas Electronics, where he leads an AI team for system-on-chip platforms. His work focuses on AI compilers, hardware-software co-design, and efficient deployment of AI models on edge devices. His current interests include using AI agents to automate hardware-aware optimization while preserving correctness, measurable performance, and reproducibility. Previously, he focused on autonomous driving at Woven by Toyota. Before moving to Japan, he worked in the United States through his own startup, Accenture, and projects supporting a federal government agency. He has focused on machine learning and advocated for digital twins since college. Talk 3: GPU profiling and inference bottlenecks Where does inference time actually go? A look at profiling GPU workloads, identifying compute and memory bottlenecks, and using low-level code changes to improve performance. The focus is on measuring improvements against a clear baseline and understanding when an optimization helps. Speaker: To be announced. Organizers Ilya Kulyatin is an entrepreneur with work and academic experience in the US, Netherlands, Singapore, UK, and Japan. He holds a BA in Economics, an MA in Finance,
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