Streaming Encoding Algorithms for Scalable Hyperdimensional Computing

• URL: http://arxiv.org/abs/2209.09868v2
• Date: Wed, 21 Sep 2022 00:45:56 GMT
• Title: Streaming Encoding Algorithms for Scalable Hyperdimensional Computing
• Authors: Anthony Thomas, Behnam Khaleghi, Gopi Krishna Jha, Sanjoy Dasgupta, Nageen Himayat, Ravi Iyer, Nilesh Jain, and Tajana Rosing
• Abstract summary: Hyperdimensional computing (HDC) is a paradigm for data representation and learning originating in computational neuroscience. In this work, we explore a family of streaming encoding techniques based on hashing. We show formally that these methods enjoy comparable guarantees on performance for learning applications while being substantially more efficient than existing alternatives.
• Score: 12.829102171258882
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hyperdimensional computing (HDC) is a paradigm for data representation and learning originating in computational neuroscience. HDC represents data as high-dimensional, low-precision vectors which can be used for a variety of information processing tasks like learning or recall. The mapping to high-dimensional space is a fundamental problem in HDC, and existing methods encounter scalability issues when the input data itself is high-dimensional. In this work, we explore a family of streaming encoding techniques based on hashing. We show formally that these methods enjoy comparable guarantees on performance for learning applications while being substantially more efficient than existing alternatives. We validate these results experimentally on a popular high-dimensional classification problem and show that our approach easily scales to very large data sets.

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