Sparse-Aware Deep Learning Accelerator
DOI:
https://doi.org/10.54097/hset.v39i.6544Keywords:
CNN; Sparsification; Sparse-Aware.Abstract
In view of the difficulty of hardware implementation of convolutional neural network computing, most of the previous convolutional neural network accelerator designs focused on solving the bottleneck of computational performance and bandwidth, ignoring the importance of convolutional neural network scarcity for accelerator design. In recent years, there are a few convolutional neural network accelerators that can take advantage of the scarcity, but they are usually difficult to consider in terms of computational flexibility, parallel efficiency and resource overhead. In view of the problem that the application of convolutional neural network (CNN) on the embedded side is limited by real-time, and there is a large degree of sparsity in CNN convolution calculation. This paper summarizes the methods of sparsification from the algorithm level and based on FPGA level. The different methods of sparsification and the research and analysis of different application layers are introduced. The advantages and development trend of sparsification are analyzed and summarized.
Downloads
References
Chen T, Du Z, Sun N, et al. DianNao: A small-footprint high-throughput accelerator for ubiquitous machine-learning[J].ACM SIGPLAN Notices,2014,49(4):269-284.
CHEN Y H, KRISHNA T, EMER J S, et al. Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks[J]. IEEE Journal of Solid-State Circuits,2017,52(1):127-138.
Lavin A, Gray S.Fast algorithms for convolutional neural networks[C]//Proc of Computer Vision and Pattern Recognition,2016:4013-4021.
Liu Xing-yu, Pool J, Han S, et al. Efficient sparse-Winograd convolutional neural networks[C]//Proc of the 6th International Conference on Learning Representations,2018:1.
Dou Jiamin, WANG Yan, Wang Yijun. Journal of Changchun University of Science and Technology (Natural Science Edition),202,45(01):72-78.
Rui Xu, Sheng Ma, Yang Guo, You Huang, Yihuang Li. Design and research of Convolutional Neural Network Accelerator based on Winograd Sparse Algorithm [J]. Computer engineering and science, 2019, 41 (09):1557-1566.
Young S I, Zhe W, Taubman D, et al. Transform Quantization for CNN Compression[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
Qiang B, Zhai Y, Zhou M, et al. SqueezeNet and Fusion Network-Based Accurate Fast Fully Convolutional Network for Hand Detection and Gesture Recognition[J]. IEEE Access, 2021, PP (99):1-1.
Abdo H, Amin K M, Hamad A M, et al. Fall Detection Based on RetinaNet and MobileNet Convolutional Neural Networks[C]// ICCES2020.2021.
C. Zhang, P. Li, G. Sun, Y. Guan, B. Xiao, and J. Cong, Optimizing fpga-based accelerator design for deep convolutional neural networks[C], in Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. ACM, 2015, pp. 161–170.
Mohammad Motamedi, Philipp Gysel, Venkatesh Akella, and Soheil Ghiasi. 2016. Design space exploration of fpga-based deep convolutional neural networks. In Design Automation Conference (ASP-DAC), 2016 21st Asia and South Paci.c. IEEE, 575–580.
Wenyan Lu, Guihai Yan, Jiajun Li, Shijun Gong, et al., FlexFlow: A Flexible Dataflow Accelerator Architecture for Convolutional Neural Networks, in IEEE International Symposium on High Performance Computer Architecture, 2017: 553-564.
Yufei Ma, Yu Cao, Sarma Vrudhula, and Jae-sun Seo. 2017. Optimizing Loop Operation and Data.ow in FPGA Acceleration of Deep Convolutional Neural Networks. In Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. ACM, 45–54.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
