Spiking neural network with RRAM: can we use it for real-world application?

The spiking neural network (SNN) provides a promising solution to drastically promote the performance and efficiency of computing systems. Previous work of SNN mainly focus on increasing the scalability and level of realism in a neural simulation, while few of them support practical cognitive applications with acceptable performance. At the same time, based on the traditional CMOS technology, the efficiency of SNN systems is also unsatisfactory. In this work, we explore different training algorithms of SNN for real-world applications, and demonstrate that the Neural Sampling method is much more effective than Spiking Time Dependent Plasticity (STDP) and Remote Supervision Method (ReSuMe). We also propose an energy efficient implementation of SNN with the emerging metal-oxide resistive random access memory (RRAM) devices, which includes an RRAM crossbar array works as network synapses, an analog design of the spike neuron, and an input encoding scheme. A parameter mapping algorithm is also introduced to configure the RRAM-based SNN. Simulation results illustrate that the system achieves 91.2% accuracy on the MNIST dataset with an ultra-low power consumption of 3.5mW. Moreover, the RRAM-based SNN system demonstrates great robustness to 20% process variation with less than 1% accuracy decrease, and can tolerate 20% signal fluctuation with about 2% accuracy loss. These results reveal that the RRAM-based SNN will be quite easy to be physically realized.