  Spiking Neural Networks are a recent and new neural network design approach
that promises tremendous improvements in power efficiency, computation
efficiency, and processing latency. They do so by using asynchronous
spike-based data flow, event-based signal generation, processing, and modifying
the neuron model to resemble biological neurons closely. While some initial
works have shown significant initial evidence of applicability to common deep
learning tasks, their applications in complex real-world tasks has been
relatively low. In this work, we first illustrate the applicability of spiking
neural networks to a complex deep learning task namely Lidar based 3D object
detection for automated driving. Secondly, we make a step-by-step demonstration
of simulating spiking behavior using a pre-trained convolutional neural
network. We closely model essential aspects of spiking neural networks in
simulation and achieve equivalent run-time and accuracy on a GPU. When the
model is realized on a neuromorphic hardware, we expect to have significantly
improved power efficiency.
