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In Situ Quantization with Memory-Transistor Transfer Unit Based on Electrochemical Random-Access Memory for Edge

Zhen Yang1, Yuxiang Yang1, Baiqian Wang2

  • 1New Cornerstone Science Laboratory, Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, China.

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Summary
This summary is machine-generated.

This study introduces a novel synaptic unit for efficient in-memory computing in neural networks. It enables on-site weight quantization for low-precision AI, significantly reducing energy consumption in edge devices.

Keywords:
ECRAMedge computingin‐memory computingweight quantization

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Artificial Intelligence

Background:

  • In-memory computing with nonvolatile synaptic arrays enhances neural network energy efficiency.
  • Current devices primarily accelerate matrix-vector multiplication, complicating hybrid unit integration due to differing training/inference needs.

Purpose of the Study:

  • To develop a compact synaptic unit for low-precision quantization calculations in neural networks.
  • To enable in situ approximate weight quantization without extra programming while retaining parallel matrix-vector multiplication (MVM) capabilities.

Main Methods:

  • Ionic nonvolatile memory-transistor coupling integration for a compact synaptic unit.
  • Utilizing the cell's physical electrical properties for an intrinsic quantization function.

Main Results:

  • Achieved classification accuracy in binary neural networks comparable to ideal quantization.
  • Demonstrated support for low-precision continual learning and mitigation of catastrophic forgetting.
  • ECRAM- and RRAM-based units showed significant energy consumption advantages (25.51× and 4.84×) over digital platforms at a 4 Mb scale.

Conclusions:

  • The developed synaptic unit offers a robust in situ quantization framework for low-precision edge training.
  • Enables efficient computations for binary/ternary large language models.
  • Presents a pathway for more energy-efficient AI hardware.