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A Sliding-Kernel Computation-In-Memory Architecture for Convolutional Neural Network.

Yushen Hu1, Xinying Xie1, Tengteng Lei1

  • 1State Key Laboratory of Advanced Displays and Optoelectronics Technologies, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology (HKUST), Hong Kong, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|October 22, 2024
PubMed
Summary
This summary is machine-generated.

A novel sliding-kernel computation-in-memory (SKCIM) architecture reduces memory access by 88%. This neuromorphic computing approach achieves over 95% accuracy in handwritten digit classification using a convolutional neural network.

Keywords:
convolutional computingconvolutional neural networkmetal‐oxideneuromorphic computingthin film transistor

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Materials Science

Background:

  • Neuromorphic computing architectures aim to mimic the human brain's efficiency.
  • Traditional systems face bottlenecks due to data movement between memory and processing units.
  • Convolutional operations are fundamental in deep learning but computationally intensive.

Purpose of the Study:

  • To introduce a novel sliding-kernel computation-in-memory (SKCIM) architecture.
  • To leverage low-temperature metal-oxide thin-film transistor (TFT) technology for monolithic integration.
  • To demonstrate the efficiency and accuracy of SKCIM for convolution tasks and neural network applications.

Main Methods:

  • Designed a SKCIM architecture with two overlapping functional arrays for memory and kernel storage.
  • Utilized low-temperature metal-oxide thin-film transistor (TFT) technology for device fabrication.
  • Implemented a 32x32 SKCIM system for convolution tasks and a 5-layer convolutional neural network for MNIST classification.

Main Results:

  • Achieved an 88% reduction in memory access operations compared to existing systems.
  • Successfully executed common convolution tasks on the 32x32 SKCIM system.
  • Attained an accuracy rate exceeding 95% on the MNIST handwritten digit dataset using the SKCIM-based CNN.

Conclusions:

  • The SKCIM architecture offers significant improvements in computational efficiency for deep learning tasks.
  • Monolithic integration using low-temperature TFT technology enables practical implementation of advanced neuromorphic systems.
  • SKCIM demonstrates high potential for accelerating AI and machine learning applications.