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FPGA based Deterministic Latency Image Acquisition and Processing System for Automated Driving Systems

机译:基于FPGA的确定性延迟图像获取和自动化驾驶系统的处理系统

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摘要

Automated driving systems are one of the key motive in latest developments in artificial intelligence and hardware technologies required for artificial intelligence. These systems have a sheer number of sensors including image sensors. These image sensors produce a huge amount of visual data. This data is generally processed using traditional image recognition and classification techniques or the latest deep learning techniques like Convolutional Neural Networks (CNNs). The accuracy and latency of the systems have great impact on how the vehicle deals with its surroundings. In this paper, we discuss how Field-Programmable Gated Array's (FPGA) flexible input/output structure enables us to implement a deterministic latency acquisition and processing system. Experimental results show that by bypassing the host CPU from the input data path overall system becomes orders of magnitude more deterministic. When the CPU is bypassed from the input data path, non-determinism in overall processing is up-to 115.5 nsec as compared to 77.4 msec when CPU is in the input path.
机译:自动化驾驶系统是人工智能所需的人工智能和硬件技术的最新动机之一。这些系统具有纯粹的传感器,包括图像传感器。这些图像传感器产生大量的可视数据。通常使用传统图像识别和分类技术或最新的深度学习技术(如卷积神经网络(CNN)处理该数据。系统的准确性和延迟对车辆如何处理其周围环境的影响很大。在本文中,我们讨论了现场可编程门控阵列的(FPGA)灵活输入/输出结构如何实现确定性延迟采集和处理系统。实验结果表明,通过将主CPU从输入数据路径旁路,总系统变为更确定性的数量级。当CPU从输入数据路径旁路时,当CPU在输入路径中时,整体处理中的非确定性为高达115.5 NSEC。

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