  Over the past decade, deep neural networks (DNNs) have demonstrated
remarkable performance in a variety of applications. As we try to solve more
advanced problems, increasing demands for computing and power resources has
become inevitable. Spiking neural networks (SNNs) have attracted widespread
interest as the third-generation of neural networks due to their event-driven
and low-powered nature. SNNs, however, are difficult to train, mainly owing to
their complex dynamics of neurons and non-differentiable spike operations.
Furthermore, their applications have been limited to relatively simple tasks
such as image classification. In this study, we investigate the performance
degradation of SNNs in a more challenging regression problem (i.e., object
detection). Through our in-depth analysis, we introduce two novel methods:
channel-wise normalization and signed neuron with imbalanced threshold, both of
which provide fast and accurate information transmission for deep SNNs.
Consequently, we present a first spiked-based object detection model, called
Spiking-YOLO. Our experiments show that Spiking-YOLO achieves remarkable
results that are comparable (up to 98%) to those of Tiny YOLO on non-trivial
datasets, PASCAL VOC and MS COCO. Furthermore, Spiking-YOLO on a neuromorphic
chip consumes approximately 280 times less energy than Tiny YOLO and converges
2.3 to 4 times faster than previous SNN conversion methods.
