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A near-threshold memristive computing-in-memory engine for edge intelligence.

Linfang Wang1,2, Weizeng Li1,3, Zhidao Zhou1,3

  • 1State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China.

Nature Communications
|July 2, 2025
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Summary
This summary is machine-generated.

This study presents a novel memristive computing-in-memory engine for edge devices. It overcomes scalability challenges using intrinsic memristor variations and advanced techniques, achieving high performance and energy efficiency.

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Electrical Engineering

Background:

  • Unconventional computing paradigms like memristive computing-in-memory and near-threshold computing offer enhanced energy efficiency and real-time performance for edge devices.
  • Scalability of these paradigms is hindered by process variations, limiting their practical application.

Purpose of the Study:

  • To develop and demonstrate a scalable 1-Mb, 16-macro near-threshold memristive computing-in-memory engine.
  • To address process variation challenges through innovative circuit design and compensation techniques.

Main Methods:

  • Utilized two-transistor-one-resistor cells with high current modulation capability (>120x resistance ratio).
  • Mitigated transistor mismatches by leveraging intrinsic memristor variations.
  • Implemented a charge stacking technique for efficient analog weight-and-combine operations.
  • Introduced an inter-macro hybrid control scheme to reduce inference power.

Main Results:

  • Fabricated chip performs highly parallel analog computing across 256 input channels with low variation (2.4% relative standard deviation).
  • Achieved a peak throughput of 10.49 tera-operations per second.
  • Demonstrated exceptional energy efficiency of up to 88.51 tera-operations per second per watt.

Conclusions:

  • The developed memristive computing-in-memory engine effectively overcomes scalability limitations caused by process variations.
  • This work paves the way for more energy-efficient and higher-performance edge computing applications.