Energy Efficient Object Detection for Automotive Applications with YOLOv3 and Approximate Hardware

Deep neural networks are the dominant models for perception tasks in the automotive domain, but their high computational complexity makes it difficult to execute them in real time with an acceptable power consumption on general-purpose devices. For this reason, the design of custom ASIC devices for real-time energy-efficient neural network deployment is a hot topic in academia and industry. Low-precision integer arithmetic and approximate computing are two popular optimizations often contemplated for saving hardware resources and power consumption. In this work, we evaluate two approximate computing circuits using different integer precisions in order to study the trade-offs that these techniques offer in terms of energy efficiency and degradation of the neural network outputs. In particular, we apply the approximations to the YOLOv3 object detection network, a popular model for critical applications in the automotive domain. By combining approximate arithmetic circuits with low precision we are able to reduce the power consumption of a MAD unit by over 50% compared to using only quantization.