SiamSNN: Siamese Spiking Neural Networks for Energy-Efficient Object Tracking

Although deep neural networks (DNNs) have achieved fantastic success in various scenarios, it's difficult to employ DNNs on many systems with limited resources due to their high energy consumption. It's well known that spiking neural networks (SNNs) are attracting more attention due to the capability of energy-efficient computing. Recently many works focus on converting DNNs into SNNs with little accuracy degradation in image classification on MNIST, CIFAR-10/100. However, few studies on shortening latency, and spike-based modules of more challenging tasks on complex datasets. In this paper, we focus on the similarity matching method of deep spike features and present a first spike-based Siamese network for object tracking called SiamSNN. Specifically, we propose a hybrid spiking similarity matching method with membrane potential and time step to evaluate the response map between exemplar and candidate images, with the same function as correlation layer in SiamFC. Then we present a coding scheme for utilizing temporal information of spike trains, and implement it in output spiking layers to improve the performance and shorten the latency. Our experiments show that SiamSNN achieves short latency and low precision loss of the original SiamFC on the tracking datasets OTB-2013, OTB-2015 and VOT2016. Moreover, SiamSNN achieves real-time (50 FPS) and extremely low energy consumption on TrueNorth.