R-SNN: Region-Based Spiking Neural Network for Object Detection

Thanks to their event-driven nature, Spiking Neural Networks (SNNs) enjoy great energy-efficiency and are believed to be a viable solution to the power wall problem. Due to difficulty in direct training, researchers have proposed indirect conversion-based methods, which allow SNNs to achieve comparable accuracy to their original non-spiking counterpart. However, most of these methods focus on classification problems; object detection with SNN, which involves a more challenging regression problem, remains an open research problem. The only existing SNN-based object detection method Spiking YOLO relied on a modified Integrate-and-Fire (IF) neuron model with two firing thresholds, which is not supported by most neuromorphic hardware. In order to run object detection on neuromorphic hardware, we propose a Region-based SNN (R-SNN) with widely adopted IF neurons. The bounding box regressor uses mirror output neurons along with the original output neurons to represent both positive and negative bounding box offsets, and decodes the output spike trains with a simple bounding box recovery algorithm to recover real-valued bounding box offsets. Moreover, we deploy our R-SNN on our neuromorphic computing system “Darwin Mouse” to develop a low-power object detection application, demonstrating the feasibility of applying our method to realworld neuromorphic hardware with limited arithmetic precision. Experiments show that our R-SNN achieves a mean average precision (mAP) of 63.1% on VOC 2007, improving the state of the art (achieved by Spiking YOLO) by more than 11%.