Adaptive spatial hashing with dual-domain memristive hardware

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.