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Simulation of a Fully Digital Computing-in-Memory for Non-Volatile Memory for Artificial Intelligence Edge

Hongyang Hu1,2, Chuancai Feng3, Haiyang Zhou1,2

  • 1State Key Laboratory of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China.

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This study introduces a digital non-volatile computing-in-memory (CIM) macro for AI edge inference. The novel design achieves high energy efficiency, demonstrating its potential for advanced AI applications.

Keywords:
NOR-Flashartificial intelligencecomputing-in-memory (CIM)convolutional neural network

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Area of Science:

  • * Electrical Engineering
  • * Computer Architecture
  • * Artificial Intelligence Hardware

Background:

  • * Digital Computing-in-Memory (CIM) is crucial for efficient AI edge inference.
  • * Digital CIM utilizing non-volatile memory (NVM) requires addressing complex device physics.
  • * Existing NVM-based CIM solutions often overlook intrinsic device characteristics.

Purpose of the Study:

  • * To propose a fully digital non-volatile CIM (DNV-CIM) macro.
  • * To integrate a compressed coding look-up table multiplier (CCLUTM) for enhanced performance.
  • * To develop a continuous accumulation scheme suitable for machine learning.

Main Methods:

  • * Implementation of a DNV-CIM macro using 40 nm technology.
  • * Integration of a novel compressed coding look-up table multiplier (CCLUTM).
  • * Application of a continuous accumulation scheme for machine learning tasks.
  • * Simulation using a modified ResNet18 network on the CIFAR-10 dataset.

Main Results:

  • * The proposed CCLUTM-based DNV-CIM macro demonstrates high compatibility with standard NOR Flash memory.
  • * Achieved a peak energy efficiency of 75.18 TOPS/W for 4-bit multiplication and accumulation (MAC) operations.
  • * Validated performance on a modified ResNet18 network for image classification.

Conclusions:

  • * The developed DNV-CIM macro offers a promising solution for energy-efficient AI edge inference.
  • * The CCLUTM design effectively addresses challenges in digital CIM with NVM.
  • * The continuous accumulation scheme enhances applicability in machine learning workloads.