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<p>Locality-sensitive hashing is a technique for approximate similarity search, but fixed thresholds and analog encoding inefficiencies have limited its hardware implementation. This work introduces a dual-domain adaptive spatial hashing (DASH) architecture on a monolithic one-transistor-one-resistor active array operating in both digital and analog domains, with an integrated multifunctional memristor. DASH performs entropy-maximized random projection and data-driven bias adaptation in the analog domain, followed by efficient Hamming-distance computation in the digital domain. Using dual-domain vector-matrix multiplication, multidimensional inputs are compressed into binary hash codes, enabling compact similarity encoding within a unified memristive hardware platform. Experimental validation on three-dimensional-clustered synthetic data shows that DASH maintains spatial separability and improves bit entropy through adaptation. Large-scale simulations on a digit dataset demonstrate improved semantic preservation, similarity recall, and noise resilience compared to non-adaptive hashing. These results position DASH as a scalable, hardware-native solution for energy-efficient, locality-aware similarity search in edge and neuromorphic systems.</p>

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Adaptive spatial hashing with dual-domain memristive hardware

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月2日

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  • Dong Hoon Shin,
  • Wonho Choi,
  • Sunwoo Cheong,
  • Néstor Ghenzi,
  • Sung Keun Shim,
  • Sung Ho Kim,
  • Kunhee Son,
  • Soo Hyung Lee,
  • Byongwoo Park,
  • Jonghoon Shin,
  • Cheol Seong Hwang

摘要

Locality-sensitive hashing is a technique for approximate similarity search, but fixed thresholds and analog encoding inefficiencies have limited its hardware implementation. This work introduces a dual-domain adaptive spatial hashing (DASH) architecture on a monolithic one-transistor-one-resistor active array operating in both digital and analog domains, with an integrated multifunctional memristor. DASH performs entropy-maximized random projection and data-driven bias adaptation in the analog domain, followed by efficient Hamming-distance computation in the digital domain. Using dual-domain vector-matrix multiplication, multidimensional inputs are compressed into binary hash codes, enabling compact similarity encoding within a unified memristive hardware platform. Experimental validation on three-dimensional-clustered synthetic data shows that DASH maintains spatial separability and improves bit entropy through adaptation. Large-scale simulations on a digit dataset demonstrate improved semantic preservation, similarity recall, and noise resilience compared to non-adaptive hashing. These results position DASH as a scalable, hardware-native solution for energy-efficient, locality-aware similarity search in edge and neuromorphic systems.