Network Group Partition and Core Placement Optimization for Neuromorphic Multi-Core and Multi-Chip Systems
文献类型：期刊论文
| 作者 | Yang, Yukuan1,2; Fan, Qihang3; Yan, Tianyi4; Pei, Jing3; Li, Guoqi5,6
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| 刊名 | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
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| 出版日期 | 2024-04-01 |
| 页码 | 16 |
| 关键词 | Multicore processing Optimization System recovery Throughput Neuromorphics Hardware Costs Network group partition core placement optimization neuromorphic chips multi-core and multi-chip systems |
| ISSN号 | 2471-285X |
| DOI | 10.1109/TETCI.2024.3379165 |
| 通讯作者 | Li, Guoqi(guoqi.li@ia.ac.cn) |
| 英文摘要 | Neuromorphic chips with multi-core architecture are considered to be of great potential for the next generation of artificial intelligence (AI) chips because of the avoidance of the memory wall effect. Deploying deep neural networks (DNNs) to these chips requires two stages, namely, network partition and core placement. For the network partition, existing schemes are mostly manual or only focus on single-layer, small-scale network partitions. For the core placement, to the best of our knowledge, there is still no work that has completely solved the communication deadlock problem at the clock-level which commonly exists in the applications of neuromorphic multi-core and multi-chip (NMCMC) systems. To address these issues that affect the operating and deployment efficiency of NMCMC systems, we formulate the network group partition problem as an optimization problem for the first time and propose a search-based network group partition scheme to solve the problem. A clock-level multi-chip simulator is established to completely avoid the deadlock problem during the core placement optimization process. What's more, a region constrained simulated annealing (RCSA) algorithm is proposed to improve the efficiency of the core placement optimization. Finally, an automated toolchain for the efficient deployment of DNNs in the NMCMC systems is developed by integrating the proposed network group partition and core placement schemes together. Experiments show the proposed group partition scheme can achieve 22.25%, 17.77%, 14.80% less in core number, 9.44%, 7.96%, 5.16% improvements in memory utilization, and more balanced communication and computation loads compared with existing manual schemes in ResNet-18, ResNet-34, and ResNet-50, respectively. In addition, the proposed core placement optimization based on the RCSA algorithm shows higher efficiency with much fewer optimization steps and can realize 9.52%, 11.91%, and 27.52% higher in throughput compared with sequential core placement without deadlock in the ResNet-18, ResNet-34, and ResNet-50 networks. This work paves the way for applying NMCMC systems to real-world scenarios to reach more powerful machine intelligence. |
| WOS关键词 | CHIP |
| 资助项目 | National Key Ramp;D Program of China |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001197905200001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Ramp;D Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/57976]  |
| 专题 | 数字内容技术与服务研究中心_听觉模型与认知计算 |
| 通讯作者 | Li, Guoqi |
| 作者单位 | 1.Chinese Acad Sci, Inst Software, Beijing 100190, Peoples R China 2.Tsinghua Univ, Ctr Brain Inspired Comp Res, Dept Precis Instrument, Beijing 100084, Peoples R China 3.Tsinghua Univ, Dept Precis Instrument, Beijing 100084, Peoples R China 4.Beijing Inst Technol, Sch Life Sci, Beijing 100081, Peoples R China 5.Chinese Acad Sci, Inst Automat, Beijing 100045, Peoples R China 6.Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Yukuan,Fan, Qihang,Yan, Tianyi,et al. Network Group Partition and Core Placement Optimization for Neuromorphic Multi-Core and Multi-Chip Systems[J]. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE,2024:16. |
| APA | Yang, Yukuan,Fan, Qihang,Yan, Tianyi,Pei, Jing,&Li, Guoqi.(2024).Network Group Partition and Core Placement Optimization for Neuromorphic Multi-Core and Multi-Chip Systems.IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE,16. |
| MLA | Yang, Yukuan,et al."Network Group Partition and Core Placement Optimization for Neuromorphic Multi-Core and Multi-Chip Systems".IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2024):16. |
入库方式： OAI收割
来源：自动化研究所
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