Delocalizing AI with Emerging Edge Intelligence (IoT/Internet)

Advances in artificial intelligence (AI)are transforming science and technologies. However, the exploding computational demands of  the most powerful AI algorithms lead to serious energy pitfalls. This trend is further accelerated by the simultaneous move towards  trillions of intelligent edge devices. Several efforts are underway to address this challenge which includes in-memory computing,  neuromorphic computing, silicon photonics architecture, etc. In this special session, we intend to showcase five eminent research talks  covering circuit-level innovation, system-level optimization, network-level creativity, and algorithmic codesign for next-generation  edge-intelligence systems. A major goal of this session is to bring communities together embarking a pathway towards decentralizing  AI in edge infrastructure with secured and energy-efficient computing.

  • Delocalized Federated Learning Over a Huge Diversity of Devices in Emerging Next-Generation Edge Intelligence Environments

  • Error Correction and Detection for Analog AI Computing at the Edge

  • Codesigning 2.5D Photonic Accelerator for Distributed Transformer at the Edge

  • A Materials- and Devices-Centric Approach Toward Neuromorphic Computing

  • Fault Tolerant In-Memory Computing based on Emerging Technologies for Ultra-Low Precision Edge AI Accelerators