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A Task-Adaptive In-Situ ReRAM Computing for Graph Convolutional Networks

文献类型：期刊论文

作者He, Yintao1,2; Li, Bing3; Wang, Ying4; Liu, Cheng1,2; Li, Huawei5; Li, Xiaowei1,2,6,7
刊名IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
出版日期2024-09-01
卷号43期号:9页码:2635-2646
关键词Task analysis Sparse matrices Convolution Convolutional neural networks Design automation Neural networks Integrated circuits Graph convolutional network hardware acceleration processing-in-memory
ISSN号0278-0070
DOI10.1109/TCAD.2024.3375251
英文摘要ReRAM-based computing-in-memory (CiM) architecture has been considered a promising solution to high-efficiency neural network accelerator, by conducting in-situ matrix multiplications and eliminating the movement of neural parameters from off-chip memory to computing units. However, we observed specific features of graph convolutional network (GCN) tasks pose design challenges to implement a high-efficiency ReRAM GCN accelerator. The ultralarge input feature data in some GCN tasks incur massive data movements, the extremely sparse adjacency matrix and input feature data involve the valid computation, and the super-large adjacency matrix that exceeds available ReRAM capacity causes frequent expensive write operations. To address the above challenges, we propose TARe, a task-adaptive CiM architecture, which consists of a hybrid in-situ computing mode to support the input feature in crossbar computing, a compact mapping scheme for efficient sparse matrix computing, and a write-free mapping to eliminate write activities in the computations with the super-large adjacency matrix. Additionally, TARe is facilitated with a task adaptive selection algorithm to generate optimized design schemes for graph neural network (GNN) tasks that have various operand sizes and data sparsity. We evaluate TARe on 11 diverse GNN tasks and compare it with different design counterparts, and the results show that achieves 168.06 $\times $ speedup and 10.95 $\times $ energy consumption reduction on average over the baseline in common GCN workloads.
资助项目National Natural Science Foundation of China (NSFC)[62090024] ; National Natural Science Foundation of China (NSFC)[62222411] ; National Natural Science Foundation of China (NSFC)[92373206] ; National Natural Science Foundation of China (NSFC)[62204164]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001297718600006
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/39609]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Li, Bing; Li, Huawei
作者单位1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Dept Comp Sci, Beijing 100190, Peoples R China
3.Capital Normal Univ, Acad Multidisciplinary Studies, Beijing 100037, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, CICS, Beijing 100190, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing 100190, Peoples R China
6.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
7.Peng Cheng Lab, Shenzhen 518066, Peoples R China
推荐引用方式
GB/T 7714		 He, Yintao,Li, Bing,Wang, Ying,et al. A Task-Adaptive In-Situ ReRAM Computing for Graph Convolutional Networks[J]. IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,2024,43(9):2635-2646.
APA He, Yintao,Li, Bing,Wang, Ying,Liu, Cheng,Li, Huawei,&Li, Xiaowei.(2024).A Task-Adaptive In-Situ ReRAM Computing for Graph Convolutional Networks.IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS,43(9),2635-2646.
MLA He, Yintao,et al."A Task-Adaptive In-Situ ReRAM Computing for Graph Convolutional Networks".IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 43.9(2024):2635-2646.

入库方式： OAI收割

来源：计算技术研究所

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