Demonstrates improved speed in hyperdimensional computing for machine learning, suggesting advances in AI hardware.
The demand for extensive computing resources and energy to support the increasing size of machine learning models has created a disparity between AI applications and the underlying hardware, hindering the advancement of advanced general intelligence. To address this challenge, researchers have been exploring new neuromorphic computing paradigms, among which Hyperdimensional Computing (HDC) stands out for its robustness to system noise, simplicity of operations, and high parallelism. However, accelerating the time-consuming encoding and search algorithms in HDC remains a formidable task.
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Liu et al. (2025) studied this question.
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