基于SRAM和NVM的存内计算技术综述

张章; 施刚; 王启帆; 马永波; 刘钢; 钱利波

doi:10.7544/issn1000-1239.202330364

基于SRAM和NVM的存内计算技术综述

张章^1,,
施刚¹,
王启帆¹,
马永波¹,
刘钢^2, ,,
钱利波³

1.
合肥工业大学微电子学院　合肥　230601
2.
上海交通大学电子信息与电气工程学院　上海　200240
3.
西安电子科技大学杭州研究院　杭州　311231

基金项目: 国家自然科学基金项目（U19A2053，62111540271）；浙江省自然科学基金项目（LDT23F04022F04）

详细信息

作者简介:
张章: 1982年生. 博士，教授. CCF高级会员. 主要研究方向为AI神经形态芯片、感存算一体、深度学习

施刚: 1999年生. 硕士研究生. 主要研究方向为深度学习、存算一体化

王启帆: 1999年生. 硕士研究生. 主要研究方向为深度学习、存算一体化

马永波: 1999年生. 硕士研究生. 主要研究方向为深度学习、存算一体化

刘钢: 1982年生. 博士，教授. 国家优秀青年基金获得者. 主要研究方向为有机光电信息功能材料和器件

钱利波: 1984年生. 博士，教授. 国家优秀青年基金获得者，华山学者特聘教授. 主要研究方向为高效功率管理集成电路、高效俘能接口集成电路与微能源系统

通讯作者:
刘钢（gang.liu@sjtu.edu.cn）

中图分类号: TP391
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出版历程
- 收稿日期: 2023-05-14
- 修回日期: 2023-09-25
- 网络出版日期: 2024-03-13
- 刊出日期: 2024-11-30

Survey of In-Memory Computing Technology Based on SRAM and Non-Volatile Memory

1.
School of Microelectronics, Hefei University of Technology, Hefei 230601
2.
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240
3.
Hangzhou Institute of Technology, Xidian University, Hangzhou 311231

Funds: This work was supported by the National Natural Science Foundation of China (U19A2053，62111540271) and the Zhejiang Provincial Natural Science Foundation of China (LDT23F04022F04).

More Information

Author Bio:
Zhang Zhang: born in 1982. PhD, professor. Senior member of CCF. His main research interests include AI neural morphology chip, sensory memory computing integration and deep learning

Shi Gang: born in 1999. Master candidate. His main research interests include deep learning and integration of storage and calculation

Wang Qifan: born in 1999. Master candidate. His main research interests include deep learning and integration of storage and calculation

Ma Yongbo: born in 1999. Master candidate. His main research interests include deep learning, and integration of storage and calculation

Liu Gang: born in 1982. PhD, professor. The national science fund for excellent young scholars. His main research interest includes organic optoelec-tronic information functional materials and devices

Qian Libo: born in 1984. PhD, professor. The national science fund for excellent young scholars, distinguished professor of Huashan scholars. His main research interests include high efficiency power management IC, and high efficiency energy capture interface IC and micro energy system

摘要

摘要:
集存储与计算于一身的快速低功耗存内计算架构，突破了存储与计算分离的传统冯·诺依曼体系，解决了限制处理器算力的“内存墙”问题，成为新型计算架构的研究热点. 存内计算的基础器件包括高速且工艺成熟的静态随机存取存储器（static RAM，SRAM）、低功耗高响应且非易失的忆阻器（memristor）、高密度低静态功耗非易失的磁性随机存取存储器（magnetic RAM，MRAM）. 研究者们基于上述器件完成大量存内计算研究，但是关于这些存内计算架构全面且系统总结的文献综述仍然缺失. 首先从SRAM、忆阻器、MRAM方向出发概述了不同器件的存内计算原理、当前存内计算架构发展状况和实际应用场景等. 然后针对当前存内计算架构存在的各种问题和挑战给出了现有解决方案和未来解决方向. 最后对基于以上器件的存内计算研究重点进行了总结并概述了目前的研究短板、展望未来的发展方向.
- 非冯·诺依曼 /
- 静态随机存取存储器 /
- 忆阻器 /
- 磁性随机存取存储器 /
- 存内计算
Abstract:
The fast and low-power in-memory computing architecture, which integrates memory and calculation, breaks through the traditional von-Neumann system that separates memory and calculation, and solves the problem of “memory wall” that limits the arithmetic power of the processor, which has become a research hotspot of new computing architecture. The basic devices for in-memory computing include fast and mature static random access memory (SRAM), low power, fast response and non-volatile memristor, and high density, low static power and non-volatile magnetic random access memory (MRAM). Up to the present, a great variety of in-memory computing studies have been proposed based on these devices, however, a systematic and comprehensive literature review on these in-memory computing architectures is still missing. In this paper, we firstly introduce the in-memory computing principles of different devices, the current development status of in-memory computing architectures, and the practical application scenarios from the three directions of SRAM, memristor, and MRAM. Next, the existing solutions and the future directions for the problems and challenges of current in-memory computing architectures are given. Finally, we summarize the research focus of in-memory computing based on the above devices, outline the shortcomings of the current research, and look forward to the future development direction.
- non-von Neumann /
- SRAM /
- memristor /
- MRAM /
- in-memory computing

HTML全文

图 1 基于SRAM的存内逻辑操作原理

Figure 1. Principles of SRAM-based in-memory logic operations

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图 2 8T SRAM的布尔运算

Figure 2. Boolean operation of 8T SRAM

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图 3 并行计算的SRAM阵列

Figure 3. SRAM array for parallel computing

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图 4 基于10T SRAM的DNN推理框架

Figure 4. 10T SRAM based DNN inference framework

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图 5 忆阻器存内逻辑计算原理

Figure 5. Calculation principle of in-memory logic of memristor

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图 6 V/R-R架构及其逻辑异或操作真值表

Figure 6. V/R-R architecture and its truth table of logic XOR operation

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图 7 常见的用于神经网络加速的忆阻器阵列

Figure 7. Common memristor array applied for neural network acceleration

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图 8 基于忆阻器的硬件神经网络

Figure 8. Hardware neural network based on memristor

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图 9 STT-MRAM的示意图

Figure 9. Schematic diagram of STT-MRAM

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图 10 近年3款MRAM芯片

Figure 10. Three MRAM chips in recent years

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图 11 8层堆叠SRAM

Figure 11. Eight-layer stack SRAM

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图 12 3维忆阻器存算芯片

Figure 12. 3D memristor memory chip

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表 1 几种常用存储器的性能对比

Table 1 Performance Comparison of Several Popular Memories

参数	SRAM	忆阻器	MRAM	DRAM	闪存
尺寸/F²	120~200	4~10	6~50	6~10	4~6
读延迟/ns	1~8	10	5~10	10~60	2.5×10⁴
写延迟/ns	8	10	12	10~60	2×10⁵
易失性	是	否	否	是	是
耐久性次数	>10¹⁵	10¹¹	>10¹⁵	>10¹⁵	10⁴~10⁵

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表 2 乘法真值表

Table 2 Multiplication Truth Table

V	W	V×W
−1	−1	1
−1	1	−1
1	−1	−1
1	1	1

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表 3 实质蕴含逻辑真值表

Table 3 Truth Table of IMP

M₁	M₂	输出
0	0	1
0	1	1
1	0	0
1	1	1

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表 4 基于忆阻器的硬件系统对比

Table 4 Comparison of Memristor-Based Hardware Systems

来源	年份	阵列尺寸	权重位数/b	工艺/nm	速度/GOPS
文献[61]	2019	1024×512	8	22	34
文献[62]	2021	256×512	3	55	10
文献[64]	2023	2048	4	130	81.92
文献[65]	2021	48×256×256	4	130	754

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表 5 几款MRAM芯片对比

Table 5 Comparison of Several MRAM Chips

来源	年份	阵列	工艺/nm	权重位数/b	能效/(TOPS·W^–1)
文献[82]	2021	64×64	28	1	262~405
文献[83]	2021	4 MB	22	2/4/8	25.1~960.2
文献[84]	2022	128×512	28	1	17.8

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