This paper introduces how to design unified GEMM with unary computing.
Present an ultra-low power unary computing architecture for GEMM, which is compatible for both temporal and rate coding.
General matrix multiplication (GEMM) is universal in various applications, such as signal processing, machine learning, and computer vision. Conventional GEMM hardware architectures based on binary computing exhibit low area and energy efficiency as …
Present uGEMM architecture, a high performance uanry computing architecture to unify the computation for both rate- and temporal-coded bit streams.
A Reconfigurable Architecture for Varying Emerging Neural Networks.