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Dong Hoon Shin1, Wonho Choi1, Sunwoo Cheong1

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Summary

This study introduces Dual-Domain Adaptive Spatial Hashing (DASH), a novel architecture for efficient similarity search. DASH enhances hardware implementations by adapting to data, improving accuracy and noise resilience for edge computing.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Materials Science

Background:

  • Locality-sensitive hashing (LSH) is crucial for approximate similarity search but faces hardware limitations due to fixed thresholds and inefficient analog encoding.
  • Existing LSH hardware implementations struggle with adaptability and energy efficiency, hindering their deployment in resource-constrained environments.

Purpose of the Study:

  • To introduce a novel Dual-Domain Adaptive Spatial Hashing (DASH) architecture for efficient and hardware-native similarity search.
  • To overcome the limitations of traditional LSH by integrating analog and digital processing with adaptive capabilities.
  • To demonstrate the effectiveness of DASH in improving accuracy, semantic preservation, and noise resilience for similarity search.

Main Methods:

  • Developed a DASH architecture on a monolithic one-transistor-one-resistor active array utilizing a multifunctional memristor.
  • Implemented dual-domain processing: entropy-maximized random projection and data-driven bias adaptation in the analog domain, followed by Hamming-distance computation in the digital domain.
  • Utilized dual-domain vector-matrix multiplication for compressing multidimensional inputs into binary hash codes.

Main Results:

  • Experimental validation on synthetic data confirmed DASH's ability to maintain spatial separability and enhance bit entropy through adaptation.
  • Large-scale simulations on a digit dataset showed improved semantic preservation, similarity recall, and noise resilience compared to non-adaptive hashing methods.
  • The unified memristive hardware platform enabled compact similarity encoding and efficient processing.

Conclusions:

  • DASH offers a scalable, hardware-native solution for energy-efficient, locality-aware similarity search.
  • The adaptive dual-domain approach significantly improves performance metrics over traditional LSH methods.
  • DASH is well-suited for deployment in edge and neuromorphic systems requiring high-performance similarity search capabilities.