An Energy-Efficient Bit-Split-and-Combination Systolic Accelerator for NAS-Based Multi-Precision Convolution Neural Networks

Dai，Liuyao; Cheng，Quan; Wang，Yuhang; Huang，Gengbin; Zhou，Junzhuo; Li，Kai; Mao，Wei; Yu，Hao

10.1109/ASP-DAC52403.2022.9712509

2022

2153-6961

978-1-6654-2136-2

Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC

2022-January

448-453

17-20 Jan. 2022

Taipei, Taiwan

Optimized convolutional neural network (CNN) models and energy-efficient hardware design are of great importance in edge-computing applications. The neural architecture search (NAS) methods are employed for CNN model optimization with multi-precision networks. To satisfy the computation requirements, multi-precision convolution accelerators are highly desired. The existing high-precision-split (HPS) designs reduce the additional logics for reconfiguration while resulting in low throughput for low precisions. The low-precision-combination (LPC) designs improve the low-precision throughput with large hardware cost. In this work, a bit-split-and-combination (BSC) systolic accelerator is proposed to overcome the bottlenecks. Firstly, BSC-based multiply-accumulate (MAC) unit is designed to support multi-precision computation operations. Secondly, multi-precision systolic dataflow is developed with improved data-reuse and transmission efficiency. The proposed work is designed by Chisel and synthesized in 28-nm process. The BSC MAC unit achieves maximum 2.40× and 1.64× energy efficiency than HPS and LPC units, respectively. Compared with published accelerator designs Gemmini, Bit-fusion and Bit-serial, the proposed accelerator achieves up to 2.94 × area efficiency and 6.38 × energy-saving performance on the multi-precision VGG-16, ResNet-18 and LeNet-5 benchmarks.

Design automation Convolution Computational modeling Neural networks Benchmark testing Throughput Energy efficiency

第一 ; 通讯

英语

EI

20221111785789

Acceleration ; Benchmarking ; Computation theory ; Convolution

Energy Conservation:525.2 ; Information Theory and Signal Processing:716.1 ; Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1

2-s2.0-85126143003

Scopus

School of Microelectronics,Southern University of Science and Technology,Shenzhen,China

Dai，Liuyao,Cheng，Quan,Wang，Yuhang,et al. An Energy-Efficient Bit-Split-and-Combination Systolic Accelerator for NAS-Based Multi-Precision Convolution Neural Networks[C],2022:448-453.