brain-inspired hyperdimensional computing: robust, scalable and energy efficient classifier

Department: Computer Science & Engineering
Faculty Advisor(s): Tajana S. Rosing

Primary Student
Name: Mohsen Imani
Phone: 619-549-9084
Grad Year: 2019

The mathematical properties of high-dimensional spaces show remarkable agreement with behaviors controlled by the brain. Brain-inspired hyperdimensional (HD) computing explores the emulation of cognition by computing with hypervectors as an alternative to computing with numbers. Hypervectors are high-dimensional (e.g., D=10,000), holographic, and (pseudo)random with independent and identically distributed (i.i.d.) components. These features provide an opportunity for robust computing in an architecture without asymmetric memory protection. We exploit such architectural insight in three widely-used methodological design approaches for developing scalable and efficient associative memories. This paper proposes architectural designs for hyperdimensional associative memory (HAM) to facilitate energy-efficient, fast, and scalable search operation using three widely-used design approaches. These HAM designs search for the nearest Hamming distance, and linearly scale with the number of dimensions in the hypervectors while exploring a large design space with orders of magnitude higher efficiency.

Industry Application Area(s)
Electronics/Photonics | Software, Analytics

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