Hybrid Chips for Training and Inference: A Unified Approach

This article explores the evolution and integration of hybrid chip architectures designed to bridge the gap between training and inference in artificial intelligence systems. The article examines architectural innovations in shared memory and compute units, thermal management advancements, and applications in edge computing scenarios. It analyzes performance optimization through specialized software stacks, including compiler innovations, runtime systems, and deployment paradigms. The article also investigates future directions in reconfigurable computing elements, non-volatile memory integration, and domain-specific acceleration, highlighting the potential for improved efficiency and adaptability in next-generation AI hardware platforms.