RRAM-Based Spiking Neural Network With Target-Modulated Spike-Timing-Dependent Plasticity.

The spiking neural network (SNN) training with spike timing-dependent plasticity (STDP) for image classification usually requires a lot of neurons to extract representative features and(or) needs an external classifier. Conventional bio-inspired learning methods do not cover all possible learning opportunities, resulting in limited performance. We propose a new bio-plausible learning rule, target-modulated STDP (TSTDP), for higher learning efficiency and accuracy. We also propose an SNN architecture trainable with TSTDP using temporally encoded spikes to obtain higher accuracy and improved area efficiency without using an external classifier. Using the MNIST dataset, we have shown that the proposed design achieves an accuracy of 92%, which is up to 7% improvement compared to conventional networks of similar sizes. For providing similar accuracy, up to 75% smaller network size has been shown on top of demonstrating stronger resilience to process variations. Benchmarking on the CIFAR-10 and neuromorphic DVS gesture datasets show an accuracy improvement of up to 12.4% and 3.6%, respectively.