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MDCIM: MRAM-Based Digital Computing-in-Memory Macro for Floating-Point Computation with High Energy Efficiency and Low Area Overhead

  • Liang Liu
  • , Lehao Tan
  • , Jie Gan
  • , Biao Pan*
  • , Jiahui Zhou
  • , Zhengliang Li
  • *Corresponding author for this work
  • Beijing Smart-Chip Microelectronics Technology Co. Ltd.
  • Beihang University

Research output: Contribution to journalArticlepeer-review

Abstract

Computing-in-Memory (CIM) is a novel computing architecture that enormously improves energy efficiency and reduces computing latency by avoiding frequent data movement between the computation and memory units. Currently, digital CIM is regarded as more suitable for high-precision operations represented in floating-point arithmetic, as it is not limited by the bit width of ADC/DAC in analog CIM. However, the development of DCIM still faces two problems: On the one hand, mainstream SRAM-based DCIM memory cells introduce large area overheads, which contain at least six transistors per cell. On the other hand, existing DCIM solutions can only support the computing precision up to FP32, failing to meet the demands of high-accuracy application scenarios. To overcome these problems, this work designs a novel SOT-MRAM-based digital CIM macro (MDCIM) with higher area/energy efficiency and achieves double-precision floating-point (FP64) computation with a modified fused multiply–accumulate (FMA) module. The proposed design is synthesized with a 55 nm CMOS technology node, achieving 0.62 mW power consumption, 26.9 GOPS/W, and 0.332 GOPS/mm2 energy efficiency at 150 MHz with 1.08 V supply. Circuit level simulation results show that the MDCIM can achieve higher area utilization compared to previous SRAM-based CIM designs.

Original languageEnglish
Article number11914
JournalApplied Sciences (Switzerland)
Volume13
Issue number21
DOIs
StatePublished - Nov 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • SOT-MRAM
  • digital computing-in-memory
  • double-precision floating-point format
  • fused multiply–accumulate

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