FullSparse: A Sparse-Aware GEMM Accelerator with Online Sparsity Prediction
Authors: 
Jiangnan Yu, Yang Fan, Hanfei Wang, Yuxuan Qiao, Zheng Wu, Xiankui Xiong, Xiao Yao, Haidong Yao, Yecheng ZhangAuthors Info & Claims
Jiangnan Yu, Yang Fan, Hanfei Wang, Yuxuan Qiao, Zheng Wu, Xiankui Xiong, Xiao Yao, Haidong Yao, Yecheng ZhangAuthors Info & ClaimsPages 298 - 301
Abstract
Leveraging sparsity optimizes storage and computation for resource-constrained devices in Deep Learning Neural Networks (DNNs). While neural networks naturally incorporate sparsity through operations like ReLU and quantization, diverse sparsity levels (0.2% to 99%) pose challenges for the design of computational units. In this paper, we provide an energy-efficient GEMM accelerator named FullSparse which is designed for diverse applications, accommodating varying sparsity levels in matrix multiplication (0.2% to 99%). This paper introduces three features for nuanced sparsity support: multi-sparsity control, predictive result sparsity, and a multi-sparsity-compatible PE array. Experimental evaluations affirm that our implementation while ensuring adaptability to sparsity, exhibits superior computational power comparable to the existing designs.
References
[1]
Jorge Albericio, Patrick Judd, Tayler Hetherington, Tor Aamodt, Natalie Enright Jerger, and Andreas Moshovos. 2016. Cnvlutin: Ineffectual-neuron-free deep neural network computing. ACM SIGARCH Computer Architecture News 44, 3 (2016), 1--13.
[2]
Jorge Albericio, Patrick Judd, Tayler Hetherington, Tor Aamodt, Natalie Enright Jerger, and Andreas Moshovos. 2016. Cnvlutin: Ineffectual-neuron-free deep neural network computing. ACM SIGARCH Computer Architecture News 44, 3 (2016), 1--13.
[3]
Yu-Hsin Chen, Joel Emer, and Vivienne Sze. 2016. Eyeriss: A spatial architecture for energy-efficient dataflow for convolutional neural networks. ACM SIGARCH computer architecture news 44, 3 (2016), 367--379.
[4]
Letian DENG and Yanru ZHAO. 2023. Deep Learning Deep Learning-Based Semantic Based Semantic Feature Extraction Feature Extraction: A Literature Review and A Literature Review and Future Directions. ZTE COMMUNICATIONS 21, 2 (2023), 11--17.
[5]
Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A Horowitz, and William J Dally. 2016. EIE: Efficient inference engine on compressed deep neural network. ACM SIGARCH Computer Architecture News 44, 3 (2016), 243--254.
[6]
Song Han, Huizi Mao, and William J Dally. 2015. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015).
[7]
Daiyi LI, Yaofeng TU, Xiangsheng ZHOU, Yangming ZHANG, and Zongmin MA. 2022. End-to-End Chinese Entity Recognition Based on BERT-BiLSTM-ATT-CRF. ZTE Communications 20, S1 (2022), 27--35.
[8]
Bert Moons and Marian Verhelst. 2016. A 0.3-2.6 TOPS/W precision-scalable processor for real-time large-scale ConvNets. In 2016 IEEE Symposium on VLSI Circuits (VLSI-Circuits). IEEE, 1--2.
[9]
Angshuman Parashar, Minsoo Rhu, Anurag Mukkara, Antonio Puglielli, Rangharajan Venkatesan, Brucek Khailany, Joel Emer, Stephen W Keckler, and William J Dally. 2017. SCNN: An accelerator for compressed-sparse convolutional neural networks. ACM SIGARCH computer architecture news 45, 2 (2017), 27--40.
[10]
Angshuman Parashar, Minsoo Rhu, Anurag Mukkara, Antonio Puglielli, Rangharajan Venkatesan, Brucek Khailany, Joel Emer, Stephen W Keckler, and William J Dally. 2017. SCNN: An accelerator for compressed-sparse convolutional neural networks. ACM SIGARCH computer architecture news 45, 2 (2017), 27--40.
[11]
Dongjoo Shin, Jinmook Lee, Jinsu Lee, and Hoi-Jun Yoo. 2017. 14.2 DNPU: An 8.1 TOPS/W reconfigurable CNN-RNN processor for general-purpose deep neural networks. In 2017 IEEE International Solid-State Circuits Conference (ISSCC). IEEE, 240--241.
[12]
Shijin Zhang, Zidong Du, Lei Zhang, Huiying Lan, Shaoli Liu, Ling Li, Qi Guo, Tianshi Chen, and Yunji Chen. 2016. Cambricon-X: An accelerator for sparse neural networks. In 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO). 1--12. https://doi.org/10.1109/MICRO.2016.7783723
[13]
Tianyun Zhang, Shaokai Ye, Kaiqi Zhang, Jian Tang, Wujie Wen, Makan Fardad, and Yanzhi Wang. 2018. A systematic dnn weight pruning framework using alternating direction method of multipliers. In Proceedings of the European conference on computer vision (ECCV). 184--199.
Index Terms
- FullSparse: A Sparse-Aware GEMM Accelerator with Online Sparsity Prediction
Recommendations
SparGD: A Sparse GEMM Accelerator with Dynamic Dataflow
Deep learning has become a highly popular research field, and previously deep learning algorithms ran primarily on CPUs and GPUs. However, with the rapid development of deep learning, it was discovered that existing processors could not meet the specific ...
Recovering sparse signals with a certain family of nonconvex penalties and DC programming
This paper considers the problem of recovering a sparse signal representation according to a signal dictionary. This problem could be formalized as a penalized least-squares problem in which sparsity is usually induced by a l1-norm penalty on the ...
Finding sparse solutions of systems of polynomial equations via group-sparsity optimization
The paper deals with the problem of finding sparse solutions to systems of polynomial equations possibly perturbed by noise. In particular, we show how these solutions can be recovered from group-sparse solutions of a derived system of linear equations. ...
Comments
Information & Contributors
Information
Published In
May 2024
345 pages
ISBN:9798400705977
DOI:10.1145/3649153
Copyright © 2024 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 02 July 2024
Check for updates
Author Tags
Qualifiers
- Extended-abstract
- Research
- Refereed limited
Conference
CF '24
Sponsor:
Acceptance Rates
CF '24 Paper Acceptance Rate 33 of 105 submissions, 31%;
Overall Acceptance Rate 273 of 785 submissions, 35%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 59Total Downloads
- Downloads (Last 12 months)59
- Downloads (Last 6 weeks)20
Reflects downloads up to 23 Oct 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in