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Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems

文献类型：期刊论文

作者Choong, Benjamin Chen Ming2; Luo, Tao3; Liu, Cheng4; He, Bingsheng5; Zhang, Wei6; Zhou, Joey Tianyi1
刊名JOURNAL OF SYSTEMS ARCHITECTURE
出版日期2022-07-01
卷号128页码:20
ISSN号1383-7621
关键词Artificial intelligence Hardware-software co-design Deep learning Embedded systems Emerging memory
DOI10.1016/j.sysarc.2022.102507
英文摘要Deep neural networks generate and process large volumes of data, posing challenges for low-resource embedded systems. In-memory computing has been demonstrated as an efficient computing infrastructure and shows promise for embedded AI applications. Among newly-researched memory technologies, racetrack memory is a non-volatile technology that allows high data density fabrication, making it a good fit for in memory computing. However, integrating in-memory arithmetic circuits with memory cells affects both the memory density and power efficiency. It remains challenging to build efficient in-memory arithmetic circuits on racetrack memory within area and energy constraints. To this end, we present an efficient in-memory convolutional neural network (CNN) accelerator optimized for use with racetrack memory. We design a series of fundamental arithmetic circuits as in-memory computing cells suited for multiply-and-accumulate operations. Moreover, we explore the design space of racetrack memory based systems and CNN model architectures, employing co-design to improve the efficiency and performance of performing CNN inference in racetrack memory while maintaining model accuracy. Our designed circuits and model-system co-optimization strategies achieve a small memory bank area with significant improvements in energy and performance for racetrack memory based embedded systems.
资助项目Joey Tianyi Zhou's SERC Central Research Fund (Use-inspired Basic Research) ; Singapore Government's Research, Innovation and Enterprise 2020 Plan (Advanced Manufacturing and Engineering domain)[A18A1b0045]
WOS研究方向Computer Science
语种英语
出版者ELSEVIER
WOS记录号WOS:000802886800003
源URL[http://119.78.100.204/handle/2XEOYT63/19592]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Luo, Tao
作者单位1.ASTAR, Ctr Frontier AI Res, 1 Fusionopolis Way,16-16 Connexis, Singapore 138632, Singapore
2.Natl Univ Singapore, Dept Elect & Comp Engn, 4 Engn Dr 3, Singapore 117583, Singapore
3.Agcy Sci Technol & Res, Inst High Performance Comp, 1 Fusionopolis Way,16-16 Connexis, Singapore 138632, Singapore
4.Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China
5.Natl Univ Singapore, Sch Comp, COM1,13 Comp Dr, Singapore 117417, Singapore
6.Hong Kong Univ Sci & Technol, Kowloon, Clear Water Bay, Hong Kong, Peoples R China
推荐引用方式
GB/T 7714		 Choong, Benjamin Chen Ming,Luo, Tao,Liu, Cheng,et al. Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems[J]. JOURNAL OF SYSTEMS ARCHITECTURE,2022,128:20.
APA Choong, Benjamin Chen Ming,Luo, Tao,Liu, Cheng,He, Bingsheng,Zhang, Wei,&Zhou, Joey Tianyi.(2022).Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems.JOURNAL OF SYSTEMS ARCHITECTURE,128,20.
MLA Choong, Benjamin Chen Ming,et al."Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems".JOURNAL OF SYSTEMS ARCHITECTURE 128(2022):20.

入库方式： OAI收割

来源：计算技术研究所

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