Frequency-Domain Inference Acceleration for Convolutional Neural Networks Using ReRAMs
文献类型：期刊论文
| 作者 | Liu, Bosheng2; Jiang, Zhuoshen2; Wu, Yalan2; Wu, Jigang2; Chen, Xiaoming3; Liu, Peng2; Zhou, Qingguo1; Han, Yinhe3 |
| 刊名 | IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
 |
| 出版日期 | 2023-12-01 |
| 卷号 | 34期号:12页码:3133-3146 |
| 关键词 | Frequency-domain accelerator energy efficiency resistive random access memory frequency-domain convolutions |
| ISSN号 | 1045-9219 |
| DOI | 10.1109/TPDS.2023.3322907 |
| 英文摘要 | Convolutional neural networks (CNNs) (including 2D and 3D convolutions) are popular in video analysis tasks such as action recognition and activity understanding. Fast algorithms such as fast Fourier transforms (FFTs) are promising in significantly reducing computation complexity by transforming convolution into frequency domain. In frequency space, conventional spatial convolutions are replaced with simpler element-wise complex multiplications. Conventional application-specific-integrated-circuit (ASIC) based frequency-domain accelerators can achieve effective performance boost but come at the cost of significant energy consumption, owing to the hierarchical memory organization. We propose a frequency-domain resistive random access memory (ReRAM) based inference accelerator called FDA that can process element-wise complex multiplication in memory for both 2D and 3D CNNs. Each ReRAM-based frequency-domain process element (PE) with two ReRAM cells can perform an element-wise complex multiplication in two continuous execution cycles. We then provide a flexible dataflow to alleviate the redundant data movements by frequency-domain data reuse and inherent symmetrical characteristic for both 2D and 3D convolutions. Evaluation results based on representative both 2D and 3D CNN benchmarks demonstrate that FDA outperforms state-of-the-art baselines with better performance and energy efficiency. |
| 资助项目 | National Natural Science Foundation of China[62302102] ; National Natural Science Foundation of China[62122076] ; National Natural Science Foundation of China[62174038] ; State Key Laboratory of Computer Architecture (ICT, CAS)[CARCHB202119] ; Guangdong Basic and Applied Basic Research Foundation[2023A1515012844] ; Guangdong Basic and Applied Basic Research Foundation[2022A1515110599] ; Guangdong Basic and Applied Basic Research Foundation[202201010347] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001091501600004 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/21107]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Wu, Jigang; Chen, Xiaoming |
| 作者单位 | 1.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China 2.Guangdong Univ Technol, Sch Comp Sci & Technol, Guangzhou 510006, Guangdong, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Bosheng,Jiang, Zhuoshen,Wu, Yalan,et al. Frequency-Domain Inference Acceleration for Convolutional Neural Networks Using ReRAMs[J]. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,2023,34(12):3133-3146. |
| APA | Liu, Bosheng.,Jiang, Zhuoshen.,Wu, Yalan.,Wu, Jigang.,Chen, Xiaoming.,...&Han, Yinhe.(2023).Frequency-Domain Inference Acceleration for Convolutional Neural Networks Using ReRAMs.IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,34(12),3133-3146. |
| MLA | Liu, Bosheng,et al."Frequency-Domain Inference Acceleration for Convolutional Neural Networks Using ReRAMs".IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 34.12(2023):3133-3146. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。