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GrapHD: Graph-Based Hyperdimensional Memorization for Brain-Like Cognitive Learning.

Prathyush Poduval1, Haleh Alimohamadi2, Ali Zakeri3

  • 1Indian Institute of Science, Bangalore, India.

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Summary
This summary is machine-generated.

This study introduces GrapHD, a brain-inspired system for hyperdimensional computing (HDC) that enhances machine learning by providing a robust graph-based memorization capability. GrapHD enables cognitive reasoning and improves learning through a novel holographic approach.

Keywords:
brain-inspired computinghyperdimensional computing (HDC)machine leaningmemorizationneuromorphic computing

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Machine Learning

Background:

  • Current deep learning algorithms lack transparent and effective memorization capabilities, hindering learning quality and reasoning.
  • Brain-inspired HyperDimensional Computing (HDC) offers a promising model for human memory, utilizing computationally tractable vectors for cognitive processes.
  • Existing methods struggle to represent and process complex relational information inherent in graph structures.

Purpose of the Study:

  • To introduce GrapHD, a novel brain-inspired system for hyperdimensional memorization of graph-based information.
  • To develop an encoding method for representing complex graph structures in high-dimensional space, supporting both weighted and unweighted graphs.
  • To enable cognitive functionalities like memory reconstruction, information retrieval, graph matching, and shortest path calculations over the encoded graph.

Main Methods:

  • Developed GrapHD, a system that represents graph relations in high-dimensional space using HDC principles.
  • Designed a holistic encoding method that distributes graph information across the entire high-dimensional representation.
  • Implemented cognitive operations including memory reconstruction, information retrieval, graph matching, and shortest path algorithms.

Main Results:

  • GrapHD significantly enhances learning algorithms by introducing short-term and long-term memorization.
  • The system facilitates cognitive computing and reasoning directly on the memorized graph structure.
  • Evaluations demonstrate substantial robustness to noise and failure, characteristic of holographic brain-like computation.

Conclusions:

  • GrapHD provides a powerful, brain-inspired approach to memorization in machine learning, particularly for graph-structured data.
  • The system enhances AI capabilities by enabling more sophisticated learning, reasoning, and cognitive functions.
  • GrapHD offers a robust and transparent alternative to existing memorization techniques in deep learning.