Exploring Hyperdimensional Associative Memory

Abstract

Brain-inspired hyperdimensional (HD) computing emulates cognition tasks by computing with hypervectors as an alternative to computing with numbers. At its very core, HD computing is about manipulating and comparing large patterns, stored in memory as hypervectors: the input symbols are mapped to a hypervector and an associative search is performed for reasoning and classification. For every classification event, an associative memory is in charge of finding the closest match between a set of learned hypervectors and a query hypervector by using a distance metric. Hypervectors with the i.i.d. components qualify a memory-centric architecture to tolerate massive number of errors, hence it eases cooperation of various methodological design approaches for boosting energy efficiency and scalability. This paper proposes architectural designs for hyperdimensional associative memory (HAM) to facilitate energy-efficient, fast, and scalable search operation using three widely-used design approaches. These HAM designs search for the nearest Hamming distance, and linearly scale with the number of dimensions in the hypervectors while exploring a large design space with orders of magnitude higher efficiency. First, we propose a digital CMOS-based HAM (D-HAM) that modularly scales to any dimension. Second, we propose a resistive HAM (R-HAM) that exploits timing discharge characteristic of nonvolatile resistive elements to approximately compute Hamming distances at a lower cost. Finally, we combine such resistive characteristic with a currentbased search method to design an analog HAM (A-HAM) that results in faster and denser alternative. Our experimental results show that R-HAM and A-HAM improve the energy-delay product by 9.6× and 1347× compared to D-HAM while maintaining a moderate accuracy of 94% in language recognition.