Hybrid Chips for Training and Inference: A Unified Approach

Author(s)	Deepika Bhatia
Country	United States
Abstract	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.
Keywords	Hybrid Chip Architecture, Edge Computing, Thermal Management, Software Optimization, Reconfigurable Computing
Field	Computer
Published In	Volume 16, Issue 1, January-March 2025
Published On	2025-03-28
Cite This	Hybrid Chips for Training and Inference: A Unified Approach - Deepika Bhatia - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2957
DOI	https://doi.org/10.71097/IJSAT.v16.i1.2957
Short DOI	https://doi.org/g896fc

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