Systolic Cube
Yongchen Wang, Ying Wang, Huawei Li, Cong Shi, Xiaowei Li · 2019
3D convolutional neural networks (CNN) are gaining popularity in action/activity analysis. Compared to 2D convolutions that share the filters in 2D spatial domain, 3D convolutions further reuse filters in the temporal dimension to capture time-domain features. Prior works on specialized 3D-CNN accelerators employ additional on-chip memories and multi-cluster architecture to reuse data among the process element (PE) arrays, which is too expensive for low-power chips. Instead of harvesting in-memory locality, we propose a 3D systolic cube architecture to exploit the spatial-and-temporal localities of 3D CNNs, which moves the reusable data in-between PEs connected via a 3D-cube Network-on-Chip. Evaluation shows that systolic-cube contributes to considerable energy-efficiency boost for activity-recognition benchmarks.