# Exploring Deep Spiking Neural Networks for Automated Driving Applications

## TL;DR

This paper explores the potential of deep spiking neural networks (SNNs) for automated driving, highlighting their low-power, event-driven advantages over traditional neural networks like CNNs and RNNs.

## Contribution

It provides an overview of recent progress in SNNs and discusses their suitability for automated driving applications.

## Key findings

- SNNs offer low-power, event-driven processing capabilities.
- SNNs are progressing towards high-efficiency hardware implementations.
- Potential advantages of SNNs for real-time automated driving tasks.

## Abstract

Neural networks have become the standard model for various computer vision tasks in automated driving including semantic segmentation, moving object detection, depth estimation, visual odometry, etc. The main flavors of neural networks which are used commonly are convolutional (CNN) and recurrent (RNN). In spite of rapid progress in embedded processors, power consumption and cost is still a bottleneck. Spiking Neural Networks (SNNs) are gradually progressing to achieve low-power event-driven hardware architecture which has a potential for high efficiency. In this paper, we explore the role of deep spiking neural networks (SNN) for automated driving applications. We provide an overview of progress on SNN and argue how it can be a good fit for automated driving applications.