標題: | Supporting Compressed-Sparse Activations and Weights on SIMD-like Accelerator for Sparse Convolutional Neural Networks |
作者: | Lin, Chien-Yu Lai, Bo-Cheng 電子工程學系及電子研究所 Department of Electronics Engineering and Institute of Electronics |
公開日期: | 1-一月-2018 |
摘要: | Sparsity is widely observed in convolutional neural networks by zeroing a large portion of both activations and weights without impairing the result. By keeping the data in a compressed-sparse format, the energy consumption could be considerably cut down due to less memory traffic. However, the wide SIMD-like MAC engine adopted in many CNN accelerators can not support the compressed input due to the data misalignment. In this work, a novel Dual Indexing Module (DIM) is proposed to efficiently handle the alignment issue where activations and weights are both kept in compressed-sparse format. The DIM is implemented in a representative SIMD-like CNN accelerator, and able to exploit both compressed-sparse activations and weights. The synthesis results with 40nm technology have shown that DIM can enhance up to 46% of energy consumption and 55.4% Energy-Delay-Product (EDP). |
URI: | http://hdl.handle.net/11536/147112 |
ISSN: | 2153-6961 |
期刊: | 2018 23RD ASIA AND SOUTH PACIFIC DESIGN AUTOMATION CONFERENCE (ASP-DAC) |
起始頁: | 105 |
結束頁: | 110 |
顯示於類別: | 會議論文 |