This article reviews recent progress in the development of the computing framework vector symbolic architectures (VSA) (also known as hyperdimensional computing). This framework is well suited for implementation in stochastic, emerging hardware, and it naturally expresses the types of cognitive operations required for artificial intelligence (AI). We demonstrate in this article that the field-like algebraic structure of VSA offers simple but powerful operations on high-dimensional vectors that can support all data structures and manipulations relevant to modern computing. In addition, we illustrate the distinguishing feature of VSA, "computing in superposition," which sets it apart from conventional computing. It also opens the door to efficient solutions to the difficult combinatorial search problems inherent in AI applications. We sketch ways of demonstrating that VSA are computationally universal. We see them acting as a framework for computing with distributed representations that can play a role of an abstraction layer for emerging computing hardware. This article serves as a reference for computer architects by illustrating the philosophy behind VSA, techniques of distributed computing with them, and their relevance to emerging computing hardware, such as neuromorphic computing.

翻译：本文综述了向量符号架构（又称超维计算）这一计算框架的最新研究进展。该框架天然适用于随机性新兴硬件的实现，并能自然表达人工智能所需的认知运算类型。本文证明，类似于域代数结构的VSA为高维向量提供了简单而强大的运算能力，可支撑现代计算中所有数据结构与操作。此外，我们阐释了VSA的核心特征——"叠加计算"，这一特性使其区别于传统计算，并为人工智能应用中固有的组合搜索难题开辟了高效求解路径。我们概述了VSA具备图灵完备性的论证方法，将其视为一种基于分布式表征的计算框架，可充当新兴计算硬件的抽象层。本文通过阐述VSA的哲学思想、分布式计算技术及其与神经形态计算等新兴硬件的关联性，为计算机体系架构研究者提供参考。