Energy efficient spiking neural network design with RRAM devices
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%).
