This article reviews recent progress in the development of the computing
framework Vector Symbolic Architectures (also known as Hyperdimensional
Computing). This framework is well suited for implementation in stochastic,
nanoscale hardware and it naturally expresses the types of cognitive operations
required for Artificial Intelligence (AI). We demonstrate in this article that
the ring-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 in modern computing. In addition, we
illustrate the distinguishing feature of Vector Symbolic Architectures,
“computing in superposition,” which sets it apart from conventional computing.
This latter property opens the door to efficient solutions to the difficult
combinatorial search problems inherent in AI applications. Vector Symbolic
Architectures are Turing complete, as we show, and we see them acting as a
framework for computing with distributed representations in myriad AI settings.
This paper serves as a reference for computer architects by illustrating
techniques and philosophy of VSAs for distributed computing and relevance to
emerging computing hardware, such as neuromorphic computing.