论文标题
高维计算的理论观点
A Theoretical Perspective on Hyperdimensional Computing
论文作者
论文摘要
高维（HD）计算是一组神经启发的方法，用于获得数据的高维，低精度，分布式数据。这些表示可以与简单的，神经合理的算法结合使用，以实现各种信息处理任务。高清计算最近引起了计算机硬件社区的浓厚兴趣，作为解决学习问题的节能，低延迟和噪音的工具。在这篇综述中，我们提出了对高清计算的理论基础的统一处理，重点是对学习的适用性。
Hyperdimensional (HD) computing is a set of neurally inspired methods for obtaining high-dimensional, low-precision, distributed representations of data. These representations can be combined with simple, neurally plausible algorithms to effect a variety of information processing tasks. HD computing has recently garnered significant interest from the computer hardware community as an energy-efficient, low-latency, and noise-robust tool for solving learning problems. In this review, we present a unified treatment of the theoretical foundations of HD computing with a focus on the suitability of representations for learning.
