  Convolutional neural networks (CNNs) are now the de facto solution for
computer vision problems thanks to their impressive results and ease of
learning. These networks are composed of layers of connected units called
artificial neurons, loosely modeling the neurons in a biological brain.
However, their implementation on conventional hardware (CPU/GPU) results in
high power consumption, making their integration on embedded systems difficult.
In a car for example, embedded algorithms have very high constraints in term of
energy, latency and accuracy. To design more efficient computer vision
algorithms, we propose to follow an end-to-end biologically inspired approach
using event cameras and spiking neural networks (SNNs). Event cameras output
asynchronous and sparse events, providing an incredibly efficient data source,
but processing these events with synchronous and dense algorithms such as CNNs
does not yield any significant benefits. To address this limitation, we use
spiking neural networks (SNNs), which are more biologically realistic neural
networks where units communicate using discrete spikes. Due to the nature of
their operations, they are hardware friendly and energy-efficient, but training
them still remains a challenge. Our method enables the training of sparse
spiking convolutional neural networks directly on event data, using the popular
deep learning framework PyTorch. The performances in terms of accuracy,
sparsity and training time on the popular DVS128 Gesture Dataset make it
possible to use this bio-inspired approach for the future embedding of
real-time applications on low-power neuromorphic hardware.
