Fig. 1: Schematics of the hash coding.
From: In-memory search with learning to hash based on resistive memory for recommendation acceleration
(left) Performing an exact comparison across all items presents a challenge due to the high computational complexity and memory requirements, thereby making the nearest neighbor search impractical for edge devices. (right) Hash coding aims to resolve this issue by mapping high-dimensional, real-valued vectors to low-dimensional binary hash codes. This is achieved by designing suitable hash functions that can preserve the similarity of these vectors in the original space. Subsequently, the similarity can be gauged using the Hamming distance of the binary hash code. This approach results in a high search speed and energy efficiency, albeit with a minor loss in accuracy.
