Custom RISC-V architecture incorporating memristive in-memory computing

被引:1
作者
Mallios, Konstantinos Alexandros [1 ]
Tompris, Ioannis [1 ]
Passias, Athanasios [1 ]
Ntinas, Vasileios [2 ]
Fyrigos, Iosif-Angelos [1 ]
Sirakoulis, Georgios Ch. [1 ]
机构
[1] Democritus Univ Thrace, Dept Elect & Comp Engn, Kimeria 67100, Xanthi, Greece
[2] Tech Univ Dresden, Inst Circuits & Syst, Fac Elect & Comp Engn, D-01069 Dresden, Germany
关键词
In-memory computing; RRAM crossbar; RISC-V; RRAM; DESIGN;
D O I
10.1016/j.aeue.2024.155505
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
Due to the rise in data-intensive applications, the von Neumann bottleneck is increasingly restricting modern computer architectures, resulting to latency and energy consumption. Addressing this challenge necessitates a CMOS-compatible solution with high energy efficiency and significant parallelism. Utilizing resistive switching components within a 1T1R crossbar array and the application of Stanford RRAM model, this paper suggests an original method for in-memory computing. Moreover, this work shows a new way to advance the popular RISC-V architecture by including memristive crossbar array. It does this by adding a custom instruction set, special hardware blocks, and the Scouting Logic Scheme. These modifications serve both as a comprehensive testbed for the memory system and a proof of concept for the future integration of memristors in computing architectures. The proposed design undergoes extensive testing and power analysis to validate its functionality and performance under various conditions. The results demonstrate significant improvements in computational efficiency and energy savings, highlighting the potential of memristor-based in-memory computing systems to overcome current architectural limitations.
引用
收藏
页数:9
相关论文
共 26 条
  • [1] Cheng Y., 1999, MOSFET MODELING BSIM
  • [2] Chua L., 2019, Handbook of Memristor Networks
  • [3] Memristor Crossbar Arrays Performing Quantum Algorithms
    Fyrigos, Iosif-Angelos
    Ntinas, Vasileios
    Vasileiadis, Nikolaos
    Sirakoulis, Georgios Ch
    Dimitrakis, Panagiotis
    Zhang, Yue
    Karafyllidis, Ioannis G.
    [J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2022, 69 (02) : 552 - 563
  • [4] Quantum Mechanical Model for Filament Formation in Metal-Insulator-Metal Memristors
    Fyrigos, Iosif-Angelos
    Ntinas, Vasileios
    Sirakoulis, Georgios Ch
    Dimitrakis, Panagiotis
    Karafyllidis, Ioannis G.
    [J]. IEEE TRANSACTIONS ON NANOTECHNOLOGY, 2021, 20 : 113 - 122
  • [5] Fyrigos IA, 2019, IEEE I C ELECT CIRC, P799, DOI [10.1109/icecs46596.2019.8965109, 10.1109/ICECS46596.2019.8965109]
  • [6] Gopal BG, 2015, Int J Eng Res Appl, V5
  • [7] Hu M, 2016, INT SYM QUAL ELECT, P374, DOI 10.1109/ISQED.2016.7479230
  • [8] Memristor Crossbar-Based Neuromorphic Computing System: A Case Study
    Hu, Miao
    Li, Hai
    Chen, Yiran
    Wu, Qing
    Rose, Garrett S.
    Linderman, Richard W.
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2014, 25 (10) : 1864 - 1878
  • [9] MAGIC-Memristor-Aided Logic
    Kvatinsky, Shahar
    Belousov, Dmitry
    Liman, Slavik
    Satat, Guy
    Wald, Nimrod
    Friedman, Eby G.
    Kolodny, Avinoam
    Weiser, Uri C.
    [J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2014, 61 (11) : 895 - 899
  • [10] Memristor-Based Material Implication (IMPLY) Logic: Design Principles and Methodologies
    Kvatinsky, Shahar
    Satat, Guy
    Wald, Nimrod
    Friedman, Eby G.
    Kolodny, Avinoam
    Weiser, Uri C.
    [J]. IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS, 2014, 22 (10) : 2054 - 2066