ERIDANUS: Efficiently Running Inference of DNNs Using Systolic Arrays

Systolic arrays with promising attributes, such as high degree of concurrent computation and high data-reuse rate, are attractive solutions for dense linear algebra. Recently, systolic arrays have been used for accelerating the inference of deep neural networks (DNNs). However, as sparsification mechanisms are applied to DNNs during or after training, DNN inference is usually a sparse problem. Therefore, it cannot fully benefit from the fundamental advantages offered by systolic arrays. To solve this challenge, we propose Eridanus, an approach to structured pruning that produces DNNs compatible with the synchronous and rhythmic flow of data from memory to systolic arrays.