DocumentCode :
254667
Title :
Energy efficient spiking neural network design with RRAM devices
Author :
Tianqi Tang ; Rong Luo ; Boxun Li ; Hai Li ; Yu Wang ; Huazhong Yang
Author_Institution :
Dept. of E.E., Tsinghua Univ., Beijing, China
fYear :
2014
fDate :
10-12 Dec. 2014
Firstpage :
268
Lastpage :
271
Abstract :
The brain-inspired neural networks have demonstrated great potential in big data analysis. The spiking neural network (SNN), which encodes the real world data into spike trains, promises great performance in computational ability and energy efficiency. Moreover, it is much more biologically plausible than the traditional artificial neural network (ANN), which keeps the input data in its original form. In this paper, we introduce an RRAM-based energy efficient implementation of STDP-based spiking neural network cascaded with ANN classifier. The recognition accuracy and power consumption are compared between SNN and traditional three-layer ANN. The experiments on the MNIST database demonstrate that the proposed RRAM-based spiking neural network requires only 14% of power consumption compared with RRAM-based artificial neural network with a slight accuracy decay (~2%).
Keywords :
neural nets; power aware computing; power consumption; resistive RAM; ANN classifier; MNIST database; RRAM devices; energy efficient spiking neural network design; power consumption; recognition accuracy; Accuracy; Artificial neural networks; Biological neural networks; Neurons; Power demand; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Integrated Circuits (ISIC), 2014 14th International Symposium on
Conference_Location :
Singapore
Type :
conf
DOI :
10.1109/ISICIR.2014.7029565
Filename :
7029565
Link To Document :
بازگشت