A Theoretical Perspective on Hyperdimensional Computing
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Hyperdimensional (HD) computing is a set of neurally inspired methods for obtaining highdimensional, low-precision, distributed representations of data. These representations can be combined with simple, neurally plausible algorithms to effect a variety of information processing tasks. HD computing has recently garnered significant interest from the computer hardware community as an energy-efficient, low-latency, and noise-robust tool for solving learning problems. In this review, we present a unified treatment of the theoretical foundations of HD computing with a focus on the suitability of representations for learning.
2021 ◽
Vol 29
(4)
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pp. 716-729
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2012 ◽
Vol 24
(24)
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pp. 2296-2299
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2013 ◽
Vol 26
(3)
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pp. 227-238
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