CMSA: Configurable Multi-directional Systolic Array for Convolutional Neural Networks

The systolic array is one of the most popular choices for convolutional neural network accelerators. However, when computing special convolution, such as small-scale convolution or depthwise convolution, the utilization rate of the array fluctuates or even declines sharply. To address these issues, we design a configurable multi-directional systolic array (CMSA). The array can switch data mapping or dataflow for special convolution by changing the data transmission direction and configuring the array. Meanwhile, it keeps the original systolic array architecture and computing mode. Our design makes the systolic array flexible. Based on our evaluation, CMSA can increase the units utilization rate by up to 1.6× compared to the typical systolic array when running last layers of ResNet. When running depthwise convolution in MobileNet, CMSA can increase the utilization rate by up to 14.8×.