  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.
