With the advent of image processing and computer vision for automotive under real-time constraints, the need for fast and architecture-optimized arithmetic operations is crucial. Alternative and efficient representations for real numbers are starting to be explored, and among them, the recently introduced posit\(^{\mathrm{TM}}\) number system is highly promising. Furthermore, with the implementation of the architecture-specific mathematical library thoroughly targeting single-instruction multiple-data (SIMD) engines, the acceleration provided to deep neural networks framework is increasing. In this paper, we present the implementation of some core image processing operations exploiting the posit arithmetic and the ARM scalable vector extension SIMD engine. Moreover, we present applications of real-time image processing to the autonomous driving scenario, presenting benchmarks on the tinyDNN deep neural network (DNN) framework.

中文翻译：

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使用posits和ARM可扩展矢量扩展的用于图像处理的快速深度神经网络

随着在实时约束下用于汽车的图像处理和计算机视觉的出现，对快速且架构优化的算术运算的需求变得至关重要。开始探索替代和有效的实数表示形式，其中包括最近引入的位置\（^ {\ mathrm {TM}} \）号码系统很有前途。此外，随着完全针对单指令多数据（SIMD）引擎的特定于体系结构的数学库的实现，提供给深度神经网络框架的加速越来越大。在本文中，我们介绍了利用posit算法和ARM可扩展矢量扩展SIMD引擎实现的一些核心图像处理操作的实现。此外，我们介绍了实时图像处理在自动驾驶场景中的应用，并在tinyDNN深层神经网络（DNN）框架上提供了基准。