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Linear Codes for Hyperdimensional Computing.

Netanel Raviv1

  • 1Washington University in St. Louis, St. Louis, MO, U.S.A. netanel.raviv@wustl.edu.

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This study introduces random linear codes to solve the challenging recovery problem in hyperdimensional computing (HDC). This novel approach enables efficient factorization of compositional representations, outperforming existing methods.

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Area of Science:

  • Computer Science
  • Information Theory
  • Machine Learning

Background:

  • Hyperdimensional computing (HDC) represents compositional information using high-dimensional vectors.
  • A key challenge in HDC is the 'recovery problem,' which involves factoring these representations into their constituent parts.

Purpose of the Study:

  • To propose a novel approach for solving the HDC recovery problem using random linear codes.
  • To demonstrate the efficacy of random linear codes for factoring bundled and bound compositional representations.

Main Methods:

  • Utilized random linear codes, which are subspaces over the Boolean field, for hyperdimensional encoding.
  • Developed recovery algorithms based on linear equation systems and subspace structures.
  • Implemented and tested techniques using Python and benchmark libraries.

Main Results:

  • Random linear codes exhibit comparable information storage to ordinary random codes.
  • These codes facilitate the creation of key-value stores, a common HDC application.
  • The proposed recovery algorithms significantly outperform exhaustive search and state-of-the-art resonator networks, often by an order of magnitude.

Conclusions:

  • Random linear codes offer a powerful and efficient solution to the HDC recovery problem.
  • This approach enhances the practical applicability of HDC in areas like machine learning and neuromorphic computing.