SENTunnel: Fast Path for Sensor Data Access on Automotive Embedded Systems.

Emerging autonomous vehicles equip multiple high-throughput sensors to enable automatic driving, such as multiline lidars and high-definition cameras. Existing automotive embedded systems usually employ software stacks to receive and preprocess high-throughput sensor data, which brings high latency and CPU consumption. Most research is devoted to accelerators for data processing but ignores the latency overhead caused by sensor data access. Therefore, this article proposes SENTunnel to build fast path from sensors to the corresponding processing units by offloading redundant software stacks into hardware. Specifically, SENTunnel builds fast path for sensor data access to processors/accelerators through two hardware modules. First, the unified access module is used to receive, parse, and transmit raw sensor data. Second, SENTunnel performs necessary preprocessing of different sensor data with the preprocessors module. Based on the design of SENTunnel, we implement a prototype for accessing the data of multiline lidars to the processor and a dedicated accelerator on FPGA. Experimental results indicate that SENTunnel reduces the latency by 55.5% for the data path to processors and reduces the CPU usage caused by the preprocessing driver by 45.9% on average. Compared to the original and partially offloaded data path to accelerators, SENTunnel reduces the latency by 93.8% and 93%, respectively, and eliminates the CPU costs. [ABSTRACT FROM AUTHOR]