Kleyko Denis, Davies Mike, Frady E Paxon, Kanerva Pentti, Kent Spencer J, Olshausen Bruno A, Osipov Evgeny, Rabaey Jan M, Rachkovskij Dmitri A, Rahimi Abbas, Sommer Friedrich T
Redwood Center for Theoretical Neuroscience at the University of California at Berkeley, CA 94720, USA and also with the Intelligent Systems Lab at Research Institutes of Sweden, 16440 Kista, Sweden.
Neuromorphic Computing Lab, Intel Labs, Santa Clara, CA 95054, USA.
Proc IEEE Inst Electr Electron Eng. 2022 Oct;110(10):1538-1571. Epub 2022 Oct 17.
This article reviews recent progress in the development of the computing framework (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 Vector Symbolic Architectures 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 Vector Symbolic Architectures, "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 Vector Symbolic Architectures 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 Vector Symbolic Architectures, techniques of distributed computing with them, and their relevance to emerging computing hardware, such as neuromorphic computing.
本文回顾了计算框架（也称为超维计算）发展的最新进展。该框架非常适合在随机的新兴硬件中实现，并且它自然地表达了人工智能（AI）所需的认知操作类型。我们在本文中证明，向量符号架构的类场代数结构为高维向量提供了简单而强大的操作，这些操作可以支持与现代计算相关的所有数据结构和操作。此外，我们阐述了向量符号架构的独特特征——“叠加计算”，这使其有别于传统计算。它还为有效解决AI应用中固有的困难组合搜索问题打开了大门。我们概述了证明向量符号架构具有计算通用性的方法。我们将它们视为一种使用分布式表示进行计算的框架，该框架可以充当新兴计算硬件的抽象层。本文通过阐述向量符号架构背后的理念、使用它们进行分布式计算的技术以及它们与诸如神经形态计算等新兴计算硬件的相关性，为计算机架构师提供参考。
