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Title: Vector Symbolic Architectures as computing framework for nanoscale hardware
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 highdimensional 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.  more » « less
Award ID(s):
1718991
NSF-PAR ID:
10486268
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
Proceedings of the IEEE
ISSN:
0018-9219
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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