Peer-reviewed paper
tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low Resolution Edge AI
tuGEMM explores temporal unary GEMM as an implementation strategy for low-resolution edge AI. The paper emphasizes area and power reductions while preserving enough arithmetic throughput for practical inference workloads.
Abstract Summary
tuGEMM explores temporal unary GEMM as an implementation strategy for low-resolution edge AI. The paper emphasizes area and power reductions while preserving enough arithmetic throughput for practical inference workloads.
Research Context
This paper contributes to my research program in GEMM, edge AI, ISCAS 2023. It is part of the broader work on efficient ML systems, hardware-software co-design, and deployment-aware computer architecture.