International Conference on Information and Knowledge Technology

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A Hybrid Method to Reduce the Voltage Consumption in the Spiking Neural Networks

Authors :

Shaghayegh Mehdizadeh saraj¹ Seyyed Amir Asghari² Mohammadreza Binesh Marvasti³

1- Kharazmi University 2- Kharazmi University 3- Kharazmi University

Keywords :

Neuron threshold،Spiking Neural Networks،Time depend coding،Artifical intelligence

Abstract :

With artificial intelligence's tremendous progress in the past decades, the demand for applying artificial intelligence algorithms and architectures in cloud computing has increased. In this regard, the need for neuromorphic hardware that enables training and processing of data generated by edge devices has increased. Different algorithms have been presented in this direction, but they consume a lot of energy and space due to the large number of calculations. Therefore, researchers tried to minimize energy consumption while maintaining accuracy in deep spiking neural networks as the least consuming generation of neural networks. In order to achieve this goal and reduce the number of references to the required memory and space, they have provided various hardware and software methods. In this article, the best architecture is used by examining the amount of energy consumed and the accuracy of different methods of architecture. Also, a hybrid method is proposed to reduce energy consumption in spiking neural networks. The proposed hybrid architecture was implemented on the MNIST dataset, showing that the power consumption is reduced by almost 1% compared to the state-of-the-art architectures. The accuracy of the proposed hybrid algorithm is 95.3%, which is the highest when compared to the architectures using the time-based coding.