RRAM-based CAM combined with time-domain circuits for hyperdimensional computing.

ABSTRACT: Content addressable memory (CAM) for search and match operations demands high speed and low power for near real-time decision-making across many critical domains. Resistive RAM (RRAM)-based in-memory computing has high potential in realizing an efficient static CAM for artificial intelligence tasks, especially on resource-constrained platforms. This paper presents an XNOR-based RRAM-CAM with a time-domain analog adder for efficient winning class computation. The CAM compares two operands, one voltage and the second one resistance, and outputs a voltage proportional to the similarity between the input query and the pre-stored patterns. Processing the summation of the output similarity voltages in the time-domain helps avoid voltage saturation, variation, and noise dominating the analog volt

SUBMITTER: Halawani Y 

PROVIDER: S-EPMC8494892 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature