Highly Efficient SNNs for High-speed Object Detection
Nemin Qiu Zhiguo Li Yuan Li Chuang Zhu
The high biological properties and low energy consumption of Spiking Neural
Networks (SNNs) have brought much attention in recent years. However, the
converted SNNs generally need large time steps to achieve satisfactory
performance, which will result in high inference latency and computational
resources increase. In this work, we propose a highly efficient and fast SNN
for object detection. First, we build an initial compact ANN by using
quantization training method of convolution layer fold batch normalization
layer and neural network modification. Second, we theoretically analyze how to
obtain the low complexity SNN correctly. Then, we propose a scale-aware
pseudoquantization scheme to guarantee the correctness of the compact ANN to
SNN. Third, we propose a continuous inference scheme by using a Feed-Forward
Integrate-and-Fire (FewdIF) neuron to realize high-speed object detection.
Experimental results show that our efficient SNN can achieve 118X speedup on
GPU with only 1.5MB parameters for object detection tasks. We further verify
our SNN on FPGA platform and the proposed model can achieve 800+FPS object
detection with extremely low latency.