A Theoretical Perspective on Hyperdimensional Computing (Extended Abstract)

Hyperdimensional (HD) computing is a set of neurally inspired methods for computing on high-dimensional, 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. We present a novel mathematical framework that unifies analysis of HD computing architectures, and provides general, non-asymptotic, sufficient conditions under which HD information processing techniques will succeed.

Authors

• Anthony Thomas a:1:{s:5:"en_US";s:12:"UC San Diego";}
• Sanjoy Dasgupta
• Tajana Rosing University of California San Diego

Keywords

• Knowledge Representation and Reasoning: Knowledge Representation Languages
• Knowledge Representation and Reasoning: Learning and reasoning
• Machine Learning: Symbolic methods