Hyperdimensional computing (HDC) is a novel computational paradigm that
operates on long-dimensional vectors known as hypervectors. The hypervectors
are constructed as long bit-streams and form the basic building blocks of HDC
systems. In HDC, hypervectors are generated from scalar values without taking
their bit significance into consideration. HDC has been shown to be efficient
and robust in various data processing applications, including computer vision
tasks. To construct HDC models for vision applications, the current
state-of-the-art practice utilizes two parameters for data encoding: pixel
intensity and pixel position. However, the intensity and position information
embedded in high-dimensional vectors are generally not generated dynamically in
the HDC models. Consequently, the optimal design of hypervectors with high
model accuracy requires powerful computing platforms for training. A more
efficient approach to generating hypervectors is to create them dynamically
during the training phase, which results in accurate, low-cost, and highly
performable vectors. To this aim, we use low-discrepancy sequences to generate
intensity hypervectors only, while avoiding position hypervectors. By doing so,
the multiplication step in vector encoding is eliminated, resulting in a
power-efficient HDC system. For the first time in the literature, our proposed
approach employs lightweight vector generators utilizing unary bit-streams for
efficient encoding of data instead of using conventional comparator-based
generators.

高维计算是一种新的计算范式，通过长向量（称为超向量）进行操作。该论文提出了一种基于轻量级向量生成器的高效编码方法，以减少计算平台的要求，并改进超向量的性能。

uHD: 轻量级和动态超高维度计算的一元处理

uHD: Unary Processing for Lightweight and Dynamic Hyperdimensional  Computing

Computing platforms in autonomous vehicles record large amounts of data from
many sensors, process the data through machine learning models, and make
decisions to ensure the vehicle's safe operation. Fast, accurate, and reliable
decision-making is critical. Traditional computer processors lack the power and
flexibility needed for the perception and machine vision demands of advanced
autonomous driving tasks. Hardware accelerators are special-purpose
coprocessors that help autonomous vehicles meet performance requirements for
higher levels of autonomy. This paper provides an overview of ML accelerators
with examples of their use for machine vision in autonomous vehicles. We offer
recommendations for researchers and practitioners and highlight a trajectory
for ongoing and future research in this emerging field.

自动驾驶汽车中的计算平台通过机器学习模型记录大量传感器数据，并进行决策以确保车辆的安全运行。传统的计算机处理器缺乏感知和机器视觉要求的能力和灵活性。硬件加速器是专用的协处理器，可帮助自动驾驶汽车满足更高级别自主性的性能要求。本文概述了机器学习加速器的用途，特别是在自动驾驶汽车的机器视觉方面。我们为研究人员和实践者提供建议，并突出了该新兴领域的持续和未来研究的轨迹。