NeuAFG: Neural Network-Based Analog Function Generator for Inference in CIM

NeuAFG: Neural Network-Based Analog Function Generator for Inference in CIM

Resistive Random-Access Memory (RRAM)-based Compute-in-Memory (CIM) architectures offer promising solutions for energy-efficient deep neural network (DNN) inference. However, conventional CIM accelerators suffer from high energy consumption due to frequent analog-to-digital (AD) and digital-to-analo...

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Bibliographic Details
Main Authors: Pengcheng Feng, Yihao Chen, Jinke Yu, Zhelong Jiang, Junjia Su, Qian Zhou, Hao Yue, Zhigang Li, Haifang Jian, Huaxiang Lu, Wan'Ang Xiao, Gang Chen
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Approximation techniques
neural network
nonlinear activation function (NAF)
resistive random-access memory (RRAM)
Online Access:https://ieeexplore.ieee.org/document/10856149/
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