Optimization Methods of Deep Neural Networks on Automotive Embedded Computing Systems

We investigate various methods of neural network optimization with the purpose of reducing runtime and maintaining performance when deployed on embedded devices. The proposed approaches follow both improvements upon the general architecture of the deep neural models, as well as possibilities of increasing hardware utilization and employing scheduling and parallelization techniques. The presented solutions significantly improve the runtime and the behaviour of neural networks when deployed on embedded targets. The optimization strategies improve the runtime by approximately 3%-4% when applied on traditional Convolutional Neural Networks. Meanwhile, the analyzed Birds-Eye View Transformer benefited from an improvement ranging from 38% up to 83% depending on the utilized hardware and the quantization range. Thus, we stress the importance of taking advantage of the available hardware resources in order to reach the best possible results in terms of execution runtime.