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GenCNN: A Partition-Aware Multi-Objective Mapping Framework for CNN Accelerators Based on Genetic Algorithm

文献类型：期刊论文

作者Mu, Yudong1,2; Fan, Zhihua1,2; Li, Wenming1,2; Zhang, Zhiyuan1,2; An, Xuejun2; Fan, Dongrui1,2; Ye, Xiaochun1,2
刊名ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION
出版日期2025-09-01
卷号22期号:3页码:26
关键词CNN Accelerator Dataflow Graph Mapping Genetic Algorithm Multi-objective Optimization
ISSN号1544-3566
DOI10.1145/3747844
英文摘要Convolutional Neural Networks (CNNs) require partitioning to efficiently run on CNN accelerators, which offer multiple parallel processing dimensions, such as Processing Element (PE) array topologies and Single Instruction Multiple Data (SIMD) execution. The choice of parallelization strategy directly impacts accelerator performance. However, the vast search space for CNN partitioning and parallelization makes manual optimization costly and complex, especially when addressing both aspects simultaneously. This highlights the need for an automated framework to efficiently map CNNs onto accelerators. Our key insight is that existing approaches suffer from inadequate accelerator performance modeling and a lack of multi-objective optimization strategies that jointly consider task partitioning and convolution parallelization. To address this, we propose GenCNN, a multi-objective genetic algorithm-based mapping framework for CNN accelerators. GenCNN first constructs a fine-grained performance model that captures both off-chip data access and on-chip data processing. It then applies the Non-dominated Sorting Genetic Algorithm II improved by Multi-Objective Bayesian Optimization to derive a Pareto-optimal partitioning and parallelization strategy that balances off-chip latency and PE utilization. Finally, GenCNN optimizes scheduling and routing to minimize data transfers. Experimental results show that GenCNN achieves up to 17.66x speedup in compilation and 6.47x in execution compared with state-of-the-art mapping frameworks.
资助项目National Key R&D Program of China[2023YFB4503500] ; Beijing Nova Program[20220484054] ; Beijing Nova Program[20230484420] ; Beijing Natural Science Foundation[L234078] ; SKLP Foundation[CLQD202502]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001606458500004
出版者ASSOC COMPUTING MACHINERY
源URL[http://119.78.100.204/handle/2XEOYT63/41576]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Fan, Zhihua; Li, Wenming
作者单位1.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Mu, Yudong,Fan, Zhihua,Li, Wenming,et al. GenCNN: A Partition-Aware Multi-Objective Mapping Framework for CNN Accelerators Based on Genetic Algorithm[J]. ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION,2025,22(3):26.
APA Mu, Yudong.,Fan, Zhihua.,Li, Wenming.,Zhang, Zhiyuan.,An, Xuejun.,...&Ye, Xiaochun.(2025).GenCNN: A Partition-Aware Multi-Objective Mapping Framework for CNN Accelerators Based on Genetic Algorithm.ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION,22(3),26.
MLA Mu, Yudong,et al."GenCNN: A Partition-Aware Multi-Objective Mapping Framework for CNN Accelerators Based on Genetic Algorithm".ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION 22.3(2025):26.

入库方式： OAI收割

来源：计算技术研究所

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