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Cognitive Correlative Encoding for Genome Sequence Matching in Hyperdimensional System
Time
Location
Event Type
Research Manuscript
Virtual Programs
Hosted in Virtual Platform
Keywords
Approximate Computing for AI/ML
Topics
Design
DescriptionIn this paper, we propose HYPERS, a novel framework supporting highly efficient and parallel pattern matching based on Hyper-Dimensional computing. HYPERS transforms inherent sequential processes of pattern matching to highly parallelizable computation tasks using HDC. HYPERS exploits HDC memorization to encode and represent the genome sequences using high-dimensional vectors. Then, it combines the genome sequences to generate an HDC reference library. During the matching, HYPERS performs alignment by exact or approximate similarity check of an encoded query with the HDC reference library. HYPERS functionality is supported by theoretical proof, verified by software implementation, and extensively tested on existing hardware platform.