Paper Info
Title
Neuromorphic circuits on segmented crossbar architectures with enhanced properties
Abstract
General purpose processors have been used in a wide variety of computational and modeling applications. However, their performance is not always sufficient when simulating neural networks, which are widely applied to signal processing and pattern recognition. In this work, after a systematic study of the computational requirements of such neural networks and an exploration of the available hardware solutions through which the aforementioned applications can be accelerated, a modern neuromorphic circuit structure is proposed with its operation attributed to memristor devices and segmented crossbar architecture. By coupling these two technologies, neuromorphic circuits have been designed with high computational performance versus integration scale and power consumption. An Ex-Situ training paradigm based on the advantageous memristor segmented crossbar is proposed, using the MNIST dataset and resulting at 97% accuracy. At the same time, a novel memristor tuning method on 1D1M configuration has been developed, able to increase the memristor programming speed.
Year
DOI
Venue
2020
10.1109/ECCTD49232.2020.9218289
2020 European Conference on Circuit Theory and Design (ECCTD)
DocType
ISBN
Citations 
Conference
978-1-7281-7183-8
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Vasileios Ntinas101.01
Panagiotis Karakolis200.34
Sirakoulis Georgios Ch.330660.41
Panagiotis Dimitrakis402.37