AdaS: A Fast and Energy-Efficient CNN Accelerator Exploiting Bit-Sparsity
Xiaolong Lin, Gang Li, Zizhao Liu, Yadong Liu, Fan Zhang, Zhuoran Song, Naifeng Jing, Xiaoyao Liang
Abstract
Bit-sparsity has shown its promise in CNN acceleration. However, prior bit-sparse accelerators have two drawbacks: 1) a large number of zero values are involved in the computation and data movement; 2) the distribution of non-zero bits is not considered in PE design. To address these issues, we propose AdaS. At the multiplier level, we dynamically serialize the operands that have fewer non-zero bits. At the dataflow level, we propose a group-wise bi-directional inner-join for workload extraction and balancing. Results show that AdaS can achieve 3.28×, 2.05× speedup, and 1.99×, 1.80× energy efficiency over Bit-Pragmatic and Laconic, respectively.