Exploring Deep Spiking Neural Networks for Automated Driving Applications
This work addresses power efficiency challenges in automated driving systems, but it is incremental as it provides an overview and argument rather than new experimental results.
The paper explores the potential of deep spiking neural networks (SNNs) for automated driving applications, arguing that their low-power, event-driven hardware architecture could address power consumption and cost bottlenecks in tasks like semantic segmentation and object detection.
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.