ReSA: Reconfigurable Systolic Array for Multiple Tiny DNN Tensors

Abstract

Systolic array architecture has significantly accelerated deep neural networks (DNNs). A systolic array comprises multiple processing elements (PEs) that can perform multiply-accumulate (MAC). Traditionally, the systolic array can execute a certain amount of tensor data that matches the size of the systolic array simultaneously at each cycle. However, hyper-parameters of DNN models differ across each layer and result in various tensor sizes in each layer. Mapping these irregular tensors to the systolic array while fully utilizing the entire PEs in a systolic array is challenging. Furthermore, modern DNN systolic accelerators typically employ a single dataflow. However, such a dataflow is not optimal for every DNN model. This work proposes ReSA, a reconfigurable dataflow architecture that aims to minimize the execution time of a DNN model by mapping tiny tensors on the spatially partitioned systolic array. Unlike conventional systolic array architectures, the ReSA data path controller enables the execution of the input, weight, and output-stationary dataflow on PEs. ReSA also decomposes the coarse-grain systolic array into multiple small ones to reduce the fragmentation issue on the tensor mapping. Each small systolic sub-array unit relies on our data arbiter to dispatch tensors to each other through the simple interconnected network. Furthermore, ReSA reorders the memory access to overlap the memory load and execution stages to hide the memory latency when tackling tiny tensors. Finally, ReSA splits tensors of each layer into multiple small ones and searches for the best dataflow for each tensor on the host side. Then, ReSA encodes the predefined dataflow in our proposed instruction to notify the systolic array to switch the dataflow correctly. As a result, our optimization on the systolic array architecture achieves a geometric mean speedup of 1.87× over the weight-stationary systolic array architecture across nine different DNN models.