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Flash-Based Computing-in-Memory Architecture to Implement High-Precision Sparse Coding.

Yueran Qi1, Yang Feng1, Hai Wang1

  • 1School of Information Science and Engineering, Shandong University, Qingdao 266237, China.

Micromachines
|December 23, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Flash-based computing-in-memory (CIM) architecture for efficient large-scale sparse coding. The proposed variation-sensitive training (VST) algorithm enables accurate image reconstruction despite hardware variations.

Keywords:
computing in memoryflash memoryimage reconstructiononline trainingsparse coding

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Area of Science:

  • Computer Engineering
  • Materials Science
  • Data Science

Background:

  • Sparse coding is crucial for big data processing but faces challenges in power consumption and efficiency.
  • Computing-in-memory (CIM) architectures offer a promising solution by integrating computation with memory.
  • Flash memory-based CIM architectures are particularly attractive for large-scale applications.

Purpose of the Study:

  • To propose a novel Flash-based CIM architecture for large-scale sparse coding.
  • To develop and optimize a variation-sensitive training (VST) algorithm for enhanced efficiency and accuracy.
  • To demonstrate the feasibility of the proposed architecture for high-precision image reconstruction.

Main Methods:

  • Implementation of a novel Flash-based CIM architecture.
  • Verification of various matrix weight training algorithms.
  • Design and optimization of the variation-sensitive training (VST) algorithm, including mapping methods and initialization conditions.
  • Comprehensive characterization considering array variations.

Main Results:

  • The proposed Flash-based CIM architecture successfully implements large-scale sparse coding.
  • The VST algorithm enhances processing efficiency and accuracy in image reconstruction applications.
  • Image reconstruction was achieved with a trained dictionary in a 55 nm flash memory array, irrespective of current variations.

Conclusions:

  • Flash-based CIM architectures are feasible for implementing high-precision sparse coding.
  • The proposed architecture and VST algorithm address power and efficiency concerns in big data processing.
  • This work paves the way for efficient sparse coding in various applications using CIM technology.