Efficient Time-Domain In-Memory Computing Based on TST-MRAM

被引:9
作者
Wang, Jinkai [1 ]
Zhang, Yue [1 ,2 ]
Lian, Chenyu [1 ]
Bai, Yining [1 ]
Huang, Zhe [1 ]
Wang, Guanda [1 ]
Zhang, Kun [1 ]
Zhang, Youguang [1 ]
Zhao, Weisheng [1 ,2 ]
机构
[1] Beihang Univ, Fert Beijing Inst, Sch Microelect, BDBC, Beijing 100191, Peoples R China
[2] Beihang Univ, Hefei Innovat Res Inst, Nanoelect Sci & Technol Ctr, Hefei 230013, Peoples R China
来源
2020 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) | 2020年
基金
中国国家自然科学基金;
关键词
In-memory computing; Time-domain; Toggle spin torque MRAM; Multi-digit addition; LOW-POWER; DESIGN; XNOR; XOR;
D O I
10.1109/iscas45731.2020.9180658
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In-memory computing is highly promising to address the processor-memory data transfer bottleneck in current computational paradigm. We firstly propose a time-domain in-memory computing (TIMC) scheme based on highspeed low-power toggle spin torque random access memory (TST-MRAM). The difference of voltage drops of bitline caused by simultaneously-activated bit-cells is reflected to time domain. Reconfigurable logic operations can be performed by utilizing D flip-flops (DFFs) to record the outputs at different moments. In order to demonstrate the advantages of this scheme in terms of speed and energy consumption, an efficient multi-digit addition circuit has been designed and analyzed. Compared with existing IMC schemes, such as spin-transfer torque computing-in-memory (STT-CiM) structure, up to 67% energy saving and 10 times delay improvement can be achieved in the case of four-digit addition by using TIMC scheme.
引用
收藏
页数:5
相关论文
共 22 条
[1]   An Efficient Heterogeneous Memristive XNOR for In-Memory Computing [J].
Abu Lebdeh, Muath ;
Abunahla, Heba ;
Mohammad, Baker ;
Al-Qutayri, Mahmoud .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2017, 64 (09) :2427-2437
[2]   X-SRAM: Enabling In-Memory Boolean Computations in CMOS Static Random Access Memories [J].
Agrawal, Amogh ;
Jaiswal, Akhilesh ;
Lee, Chankyu ;
Roy, Kaushik .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2018, 65 (12) :4219-4232
[3]   Design and Evaluation of a Spintronic In-Memory Processing Platform for Nonvolatile Data Encryption [J].
Angizi, Shaahin ;
He, Zhezhi ;
Bagherzadeh, Nader ;
Fan, Deliang .
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2018, 37 (09) :1788-1801
[4]  
Bui HT, 2002, IEEE T CIRCUITS-II, V49, P25, DOI 10.1109/82.996055
[5]  
Chen WH, 2018, ISSCC DIG TECH PAP I, P494, DOI 10.1109/ISSCC.2018.8310400
[6]   PRIME: A Novel Processing-in-memory Architecture for Neural Network Computation in ReRAM-based Main Memory [J].
Chi, Ping ;
Li, Shuangchen ;
Xu, Cong ;
Zhang, Tao ;
Zhao, Jishen ;
Liu, Yongpan ;
Wang, Yu ;
Xie, Yuan .
2016 ACM/IEEE 43RD ANNUAL INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE (ISCA), 2016, :27-39
[7]   Computing in Memory With Spin-Transfer Torque Magnetic RAM [J].
Jain, Shubham ;
Ranjan, Ashish ;
Roy, Kaushik ;
Raghunathan, Anand .
IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS, 2018, 26 (03) :470-483
[8]  
Kang M., 2016, IEEE J EM SEL TOP C, V10, P494
[9]   An In-Memory VLSI Architecture for Convolutional Neural Networks [J].
Kang, Mingu ;
Lim, Sungmin ;
Gonugondla, Sujan ;
Shanbhag, Naresh R. .
IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, 2018, 8 (03) :494-505
[10]   Low-Power and Fast Full Adder by Exploring New XOR and XNOR Gates [J].
Naseri, Hamed ;
Timarchi, Somayeh .
IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS, 2018, 26 (08) :1481-1493