Memristor-based Deep Convolution Neural Network: A Case Study

2018/09/14 by Fan Zhang, Zhang, Fan, Miao Hu +1 · 1 voice · 1 citation
Computer Science · Engineering · Mathematics · #Advanced Memory and Neural Computing #Advanced Neural Network Applications #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Neural and Evolutionary Computing (cs.NE) #cs.ET #cs.LG #cs.NE #stat.ML

paper · pdf · doi:10.48550/arxiv.1810.02225

openalex publication_date 2018/09/14 · arxiv published 2018/09/14 · arxiv updated 2018/09/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper, we firstly introduce a method to efficiently implement large-scale high-dimensional convolution with realistic memristor-based circuit components. An experiment verified simulator is adapted for accurate prediction of analog crossbar behavior. An improved conversion algorithm is developed to convert convolution kernels to memristor-based circuits, which minimizes the error with consideration of the data and kernel patterns in CNNs. With circuit simulation for all convolution layers in ResNet-20, we found that 8-bit ADC/DAC is necessary to preserve software level classification accuracy.

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Deep Learning with Limited Numerical Precision
• Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
• Neuro-Inspired Computing With Emerging Nonvolatile Memorys

Cited by

• Mitigate Parasitic Resistance in Resistive Crossbar-based Convolutional Neural Networks

Discussions

• Memristor-Based Deep Convolution Neural Network: A Case Study [hn, 3 points, 0 comments]

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