This article focuses on the trends, opportunities, and challenges of novel arithmetic for deep neural network (DNN) signal processing, with particular reference to assisted- and autonomous driving applications. Due to strict constraints in terms of the latency, dependability, and security of autonomous driving, machine perception (i.e., detection and decision tasks) based on DNNs cannot be implemented by relying on remote cloud access. These tasks must be performed in real time in embedded systems on board the vehicle, particularly for the inference phase (considering the use of DNNs pretrained during an offline step). When developing a DNN computing platform, the choice of the computing arithmetic matters. Moreover, functional safe applications, such as autonomous driving, impose severe constraints on the effect that signal processing accuracy has on the final rate of wrong detection/decisions. Hence, after reviewing the different choices and tradeoffs concerning arithmetic, both in academia and industry, we highlight the issues in implementing DNN accelerators to achieve accurate and lowcomplexity processing of automotive sensor signals (the latter coming from diverse sources, such as cameras, radar, lidar, and ultrasonics). The focus is on both general-purpose operations massively used in DNNs, such as multiplying, accumulating, and comparing, and on specific functions, including, for example, sigmoid or hyperbolic tangents used for neuron activation.

中文翻译：

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自动驾驶深度神经网络信号处理的新算法：挑战与机遇

本文重点介绍深度神经网络 (DNN) 信号处理的新算法的趋势、机遇和挑战，特别是辅助和自动驾驶应用。由于自动驾驶在时延、可靠性和安全性等方面的严格限制，基于DNN的机器感知（即检测和决策任务）无法依靠远程云访问来实现。这些任务必须在车载嵌入式系统中实时执行，特别是在推理阶段（考虑使用在离线步骤中预训练的 DNN）。在开发 DNN 计算平台时，计算算法的选择很重要。此外，功能安全应用，如自动驾驶，对信号处理精度对最终错误检测/决策率的影响施加了严格的限制。因此，在回顾了学术界和工业界关于算术的不同选择和权衡之后，我们强调了在实现 DNN 加速器以实现汽车传感器信号（后者来自不同来源，如相机、雷达、激光雷达和超声波）。重点是在 DNN 中大量使用的通用操作，例如乘法、累加和比较，以及特定函数，例如用于神经元激活的 sigmoid 或双曲正切。我们强调了实施 DNN 加速器以实现汽车传感器信号（后者来自不同来源，如相机、雷达、激光雷达和超声波）的准确和低复杂度处理的问题。重点是在 DNN 中大量使用的通用操作，例如乘法、累加和比较，以及特定函数，例如用于神经元激活的 sigmoid 或双曲正切。我们强调了实施 DNN 加速器以实现汽车传感器信号（后者来自不同来源，如相机、雷达、激光雷达和超声波）的准确和低复杂度处理的问题。重点是在 DNN 中大量使用的通用操作，例如乘法、累加和比较，以及特定函数，例如用于神经元激活的 sigmoid 或双曲正切。