Alert button
Picture for Wenzhi Fu

Wenzhi Fu

Alert button

S2Engine: A Novel Systolic Architecture for Sparse Convolutional Neural Networks

Jun 15, 2021
Jianlei Yang, Wenzhi Fu, Xingzhou Cheng, Xucheng Ye, Pengcheng Dai, Weisheng Zhao

Figure 1 for S2Engine: A Novel Systolic Architecture for Sparse Convolutional Neural Networks
Figure 2 for S2Engine: A Novel Systolic Architecture for Sparse Convolutional Neural Networks
Figure 3 for S2Engine: A Novel Systolic Architecture for Sparse Convolutional Neural Networks
Figure 4 for S2Engine: A Novel Systolic Architecture for Sparse Convolutional Neural Networks

Convolutional neural networks (CNNs) have achieved great success in performing cognitive tasks. However, execution of CNNs requires a large amount of computing resources and generates heavy memory traffic, which imposes a severe challenge on computing system design. Through optimizing parallel executions and data reuse in convolution, systolic architecture demonstrates great advantages in accelerating CNN computations. However, regular internal data transmission path in traditional systolic architecture prevents the systolic architecture from completely leveraging the benefits introduced by neural network sparsity. Deployment of fine-grained sparsity on the existing systolic architectures is greatly hindered by the incurred computational overheads. In this work, we propose S2Engine $-$ a novel systolic architecture that can fully exploit the sparsity in CNNs with maximized data reuse. S2Engine transmits compressed data internally and allows each processing element to dynamically select an aligned data from the compressed dataflow in convolution. Compared to the naive systolic array, S2Engine achieves about $3.2\times$ and about $3.0\times$ improvements on speed and energy efficiency, respectively.

* IEEE Transactions on Computers, 2021  
* 13 pages, 17 figures 
Viaarxiv icon