Model-Based Development Considering Self-Driving Systems for Many-Core Processors

摘要

Embedded systems, such as self-driving systems, consist of multiple applications interacting in a complex way. Many-core processors can execute high-load arithmetic processing for self-driving systems with low power consumption. Applications must be parallelized to achieve high-speed processing with many-core processors; however, manual parallelization is difficult. Model-based development makes it possible to automate the parallelization of one application (model) for many-core processors. However, a system composed of multiple models, such as self-driving systems, cannot be parallelized for many-core processors. In this paper, we propose a model-based parallelization method compatible with the Robot Operating System for parallelizing a system composed of multiple models. Experimental evaluation revealed that the code generated by the proposed method has the same performance as those manually written by the code. In addition, we propose a data parallelization method to support a model that inputs very large data, such as a self-driving system. The evaluation demonstrates that the proposed method improves the data parallelism of the Simulink model.