Vehicle Motion Control on SIMD: Traditional and AI based models on the edge

Abstract

Recent advancements in technology such as Artificial Intelligence (AI) and Non- Linear Model Predictive Control (NMPC) have led to its use in the field of motion control in vehicles. When it comes to the implementation of the models related to these technologies, they are expected to be executed within hard timing deadlines as they are performance critical. Further, due to their high computational cost, coupled with the strict deadlines, they are usually deployed on accelerators like the Graphics Processing Unit (GPU). However, resource-constrained embedded platforms cannot afford to have such accelerators. Therefore considering these limitations, it’s crucial to thoroughly investigate the implementation of these models entirely on CPU without any dedicated accelerator, while meeting the strict requirements. This thesis investigates the method by analyzing two different models, viz. AI and NMPC models, in which the Single Instruction Multiple Data (SIMD) component of an Arm processor is exploited. The SIMD units are commonly used for vector operations in a modern CPU. By using these models, various Arm’s SIMD implementation techniques such as Arm Neon intrinsics, Ne10 library and Auto-vectorization are investigated. When compared to the traditional approach of sequential computing implementation, the proposed method implemented with Neon Intrinsics was found to be more efficient and gave an execution time reduction of 61.9% for an AI model, while the NMPC model gave an increase in execution time of 8.3%.