In recent years, machine learning (ML) and neural networks (NNs) have gained widespread use and attention across various domains, particularly in transportation for achieving autonomy, including the emergence of flying taxis for urban air mobility (UAM). However, concerns about certification have come up, compelling the development of standardized processes encompassing the entire ML and NN pipeline. This paper delves into the inference stage and the requisite hardware, highlighting the challenges associated with IEEE 754 floating-point arithmetic and proposing alternative number representations. By evaluating diverse summation and dot product algorithms, we aim to mitigate issues related to non-associativity. Additionally, our exploration of fixed-point arithmetic reveals its advantages over floating-point methods, demonstrating significant hardware efficiencies. Employing an empirical approach, we ascertain the optimal bit-width necessary to attain an acceptable level of accuracy, considering the inherent complexity of bit-width optimization.

翻译：近年来，机器学习（ML）和神经网络（NN）在各领域获得了广泛应用与关注，尤其是在实现自主性的交通运输领域，包括城市空中交通（UAM）中飞行出租车的兴起。然而，认证问题随之出现，迫使开发涵盖整个ML和NN管道的标准化流程。本文深入探讨推理阶段及其所需硬件，重点阐述与IEEE 754浮点运算相关的挑战，并提出替代数值表示方法。通过评估多种求和与点积算法，我们旨在缓解与非结合性相关的问题。此外，我们对定点算术的探索揭示了其相对于浮点方法的优势，展现出显著的硬件效率。采用经验性方法，考虑到位宽优化的固有复杂性，我们确定了达到可接受精度所需的最佳位宽。