Edge AI refers to the deployment of artificial intelligence models directly on edge devices such as sensors, cameras, mobile devices, or embedded systems, rather than relying on centralized cloud infrastructure. It enables local data processing, reducing latency, bandwidth usage, and privacy risks. Edge AI systems often use optimized models and hardware accelerators to operate under constraints of power, memory, and compute, supporting real-time applications in areas such as autonomous systems, industrial monitoring, and smart environments.

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Eric C. Blow to Deliver Photonic AI Keynote at COOL Chips 29 in Tokyo on April 17th

Eric C. Blow of NEC Laboratories America presents a keynote at COOL Chips 29 in Tokyo, exploring multi-modal photonic computing for real-time, ultra-efficient inference. This work highlights how photonics is reshaping AI performance, enabling faster and more energy-efficient processing across next-generation systems.