Hardware architecture for optimized neural network-based inference on autonomous driving systems
Aleksander Gjikopulli
Hardware architecture for optimized neural network-based inference on autonomous driving systems.
Rel. Maurizio Martina. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Elettronica (Electronic Engineering), 2019
Abstract
The majority of automotive original equipment manufacturers (OEMs) are including one or more systems that assist the driving task by avoiding collisions, warning the presence of obstacles, detecting road signs, constantly monitoring the driver for fatigue detection, etc. All these system are collected under a category called Advanced Driver-Assistant Systems (ADAS) that builds the foundation for developing the full autonomous vehicle: a model of vehicle that is able to drive itself. Advancements in this domain are empowered by the use of neural networks, which have demonstrated unprecedented levels of accuracy compared to the standard computer vision algorithms used before. The computation intensity for running neural networks is high and thus, different hardware platforms have emerged on the market outperforming the classic GPUs used in precedence in terms of intensity and power consumption.
This thesis, developed in partnership with Marelli, discusses possible hardware solutions that will facilitate advanced assistance functionalities to be present not only on high-end car, like usually appears to be the market today, but also on less expensive or low-end cars
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