Proposing a Fast and Scalable Systolic Array for Matrix Multiplication
Bahar Asgari, Ramyad Hadidi, Hyesoon Kim · 2020
Matrix multiplication (MM) has several applications in fields such as statistics, physics, economics, and computer science. For instance, the main computation behind deep neural networks (DNNs) is a convolution that can be implemented as MM. The increasing demand for executing MM quickly has motivated several proposals to design specialized hardware for it. Among the proposals, systolic arrays [1] [2] [3] [4] [5] have seen significant interest mainly because of their unique interconnections that satisfies the unique requirement of data reuse in MM. Systolic arrays are networks of connected compute units that independently compute partial results as a function of the input and pass data within their structure.