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Resistive memory-based neural differential equation solver for score-based diffusion model.

Jichang Yang1,2,3, Hegan Chen1,2,3, Jia Chen4

  • 1Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong, China.

Nature Communications
|May 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel brain-inspired analog in-memory computing system for faster and more energy-efficient AI content generation. The new hardware significantly accelerates generative AI tasks while reducing power consumption compared to digital systems.

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Area of Science:

  • Artificial Intelligence
  • Computer Engineering
  • Neuroscience

Background:

  • Current AI-generated content (AIGC) models, like score-based diffusion, are computationally expensive and slow.
  • Digital computers face bottlenecks due to separate storage and processing, hindering continuous generation dynamics.

Purpose of the Study:

  • To develop a more efficient hardware solution for generative AI.
  • To overcome the limitations of digital computing in AI content generation.

Main Methods:

  • Implemented a brain-inspired analog in-memory computing system using resistive memory.
  • Integrated storage and computation for time-continuous neural differential equation solving.
  • Experimentally validated the system using 180 nm resistive memory macros.

Main Results:

  • Achieved significant acceleration in unconditional (69.0x) and conditional (116.5x) generation tasks compared to digital hardware.
  • Reduced energy consumption by 31.5% and 52.0% for unconditional and conditional generation, respectively.
  • Maintained generative quality equivalent to software baselines.

Conclusions:

  • The analog in-memory computing system offers a promising hardware acceleration for generative AI.
  • This approach enhances efficiency for edge computing applications in generative AI.
  • Brain-inspired computing architectures can overcome limitations of conventional digital systems for AI tasks.