Back to Search Start Over

Non-spike timing-dependent plasticity learning mechanism for memristive neural networks.

Authors :
Tang, Zhiri
Chen, Yanhua
Wang, Zhihua
Hu, Ruihan
Wu, Edmond Q.
Source :
Applied Intelligence; Jun2021, Vol. 51 Issue 6, p3684-3695, 12p
Publication Year :
2021

Abstract

Memristive neural networks (MNNs) attract the attention of many researchers because memristor can mimic the learning mechanism of biologic neuron, spike timing-dependent plasticity (STDP). While STDP brings huge potentials on many applications for memristive neural networks, it also gives complex calculation process for hardware implement. In this work, a non-STDP learning mechanism is proposed, which is implemented in two common frameworks including feedforward neural network and crossbar. The non-STDP learning mechanism relies on the linear relationship between the value of memristor and area of input spikes, which gives the proposed method a simple calculation process and better hardware compatibility. Experimental results show that the non-STDP learning mechanism can help to achieve good hardware performance in both feedforward neural network and crossbar frameworks. Compared with STDP based memristive neural networks, the proposed method can save 2.19%-24.4% hardware resource (ALMs) and improve 1.56-12.25 MHz processing speed under a set of different network scales. In future, some other complex memristor models with non-STDP learning mechanism should be taken into consideration, which will give more room for practical applications of memristive neural networks. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0924669X
Volume :
51
Issue :
6
Database :
Complementary Index
Journal :
Applied Intelligence
Publication Type :
Academic Journal
Accession number :
150393150
Full Text :
https://doi.org/10.1007/s10489-020-01985-w