Name
Abstract | ||
|---|---|---|
Compact convolutional neural networks have become a hot research topic. However, we find that the systolic array accelerators are extremely inefficient in dealing with compact models, especially when processing depthwise convolutional layers in the neural networks. To make systolic arrays more efficient for compact convolutional neural networks, we propose the heterogeneous systolic array (HeSA) architecture. It introduces heterogeneous processing elements that support multiple dataflows, which can further exploit the reuse data chance of depthwise convolutional layers and without changing the structure of the naïve systolic array. By increasing the utilization rate of processing elements in the array, the HeSA improves the performance, throughput, and energy efficiency compared to the standard baseline. In addition, we design the flexible buffer structure for the HeSA. Through configuring it, the HeSA can allocate bandwidth flexibly to maintaining high performance and low communication cost. Based on our evaluation with typical workloads, the HeSA improves the utilization rate of the computing resource in depthwise convolutional layers by 4.5× - 11.2× and acquires 1.6 - 3.1× total performance speedup compared to the standard systolic array architecture. In the large-scale array design, the HeSA can reduce the data traffic by 40% while maintaining the same performance as the scaling-out method. By improving the on-chip data reuse opportunities and reducing data traffic, the HeSA saves over 20% in energy consumption. Meanwhile, the area of the HeSA is basically unchanged compared to the baseline due to its simple design. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TPDS.2021.3129647 | IEEE Transactions on Parallel and Distributed Systems |
Keywords | DocType | Volume |
Hardware accelerator,architecture,convolutional neural network,depthwise separable convolution,systolic array | Journal | 33 |
Issue | ISSN | Citations |
11 | 1045-9219 | 0 |
PageRank | References | Authors |
0.34 | 10 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Xu | 1 | 0 | 0.34 |
| Sheng Ma | 2 | 0 | 0.34 |
| Yaohua Wang | 3 | 44 | 14.23 |
| Yang Guo | 4 | 67 | 32.72 |
| Dongsheng Li | 5 | 299 | 60.22 |
| Yuran Qiao | 6 | 0 | 0.34 |