RDMA-Based Deterministic Communication Architecture for Autonomous Driving

Abstract

Autonomous driving is a big challenge for nextgeneration vehicles and requires multiple computationallyintensive deep neural networks (DNNs) to be implemented on distributed automotive platforms. Distributed software-enabling autonomous functionalities-has strict timing requirements, e.g., low and deterministic end-to-end latency. Such timings rely on the communication technologies used in the automotive platform, as much on the computation performance of CPUs, GPUs, TPUs, and FPGAs. Hence, we advocate the use of Remote Direct Memory Access (RDMA) technology-typically used in data centers-in automotive platforms. As shown by our experiments with real hardware, Soft-RoCE (software implementation of RDMA) offers low latency communication because of minimal CPU involvement and reduced memory copies. Simultaneously, we show that the native implementation of RDMA does not support determinism, i.e., there is a high variation in communication delays in the presence of interfering data packets. To mitigate this issue, we propose a multi-layer communication stack comprising a deterministic scheduler on top of the Soft-RoCE layer. Further, we have developed a C++ library that offers easy-to-use communication interfaces for distributed applications while implementing the proposed architecture. Experiments show that our library (i) reduces the end-to-end latency of distributed object detection by nearly 9% while having an implementation overhead of less than 1.5% and (ii) minimizes the effects of other data traffic on the delay in high-priority communication.