Enabling energy-Efficient object detection with surrogate gradient descent in spiking neural networks. (arXiv:2310.12985v1 [cs.CV])

Spiking Neural Networks (SNNs) are a biologically plausible neural network
model with significant advantages in both event-driven processing and
spatio-temporal information processing, rendering SNNs an appealing choice for
energyefficient object detection. However, the non-differentiability of the
biological neuronal dynamics model presents a challenge during the training of
SNNs. Furthermore, a suitable decoding strategy for object detection in SNNs is
currently lacking. In this study, we introduce the Current Mean Decoding (CMD)
method, which solves the regression problem to facilitate the training of deep
SNNs for object detection tasks. Based on the gradient surrogate and CMD, we
propose the SNN-YOLOv3 model for object detection. Our experiments demonstrate
that SNN-YOLOv3 achieves a remarkable performance with an mAP of 61.87% on the
PASCAL VOC dataset, requiring only 6 time steps. Compared to SpikingYOLO, we
have managed to increase mAP by nearly 10% while reducing energy consumption by
two orders of magnitude.