Year: 2025
Type: article
Abstract: Memristor crossbar-based neural network systems offer high throughput with low energy consumption. A key advantage of on-chip training in these systems is their ability to mitigate the effects of device variability and faults. This paper presents an efficient on-chip training circuit for memristor crossbar-based multi-layer neural networks. We propose a novel method for storing the product of two analog signals directly in a memristor device, eliminating the need for ADC and DAC converters. Experimental results show that the proposed system is approximately twice as energy efficient and 1.5 times faster than existing memristor-based systems for training multi-layer neural networks.
Source: Chips
DOI: 10.3390/chips4030038
Authors: Raqibul Hasan, Md Shahanur Alam, Tarek M. Taha
Institutions: Independent University, Bangladesh, University of Dayton
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,207
OpenAlex ID: W4414013279