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Updated: May 24, 2025

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A full-stack memristor-based computation-in-memory system with software-hardware co-development.

Ruihua Yu1, Ze Wang1, Qi Liu1

  • 1School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China.

Nature Communications
|March 3, 2025
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Summary
This summary is machine-generated.

This study introduces a software-hardware co-development approach for memristor-based computation-in-memory (CIM) systems. This method enhances flexibility and efficiency, improving neural network accuracy during training and inference.

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Area of Science:

  • Computer Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Memristor-based computation-in-memory (CIM) systems face limitations in practicality due to hardware design constraints and manual parameter tuning.
  • Optimizing CIM systems is crucial for efficient and robust artificial intelligence hardware.

Purpose of the Study:

  • To develop a software-hardware co-development approach for enhancing the flexibility and efficiency of memristor-based CIM systems.
  • To improve the robustness of neural network models against hardware non-idealities and analogue computing noise.

Main Methods:

  • Implemented a flexible hardware component supporting diverse dataflow and mapping strategies.
  • Developed software for automatic model placement and efficient optimization techniques.
  • Integrated software and hardware for a full-stack co-development solution.

Main Results:

  • Demonstrated the system on six neural network models across four tasks.
  • Achieved a 4.76% accuracy improvement for ResNet-32 during training.
  • Observed 3.32% to 9.45% accuracy improvements across models during on-chip inference.

Conclusions:

  • The proposed software-hardware co-development approach significantly improves the performance and practicality of memristor-based CIM systems.
  • Automatic optimization methods enhance model robustness and suppress noise, leading to higher accuracy in AI applications.