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LOCATION:Level 2 Lobby
DTSTART;TZID=America/Los_Angeles:20250623T180000
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UID:dac_DAC 2025_sess287_LBR114@linklings.com
SUMMARY:Late Breaking Results: On-the-Fly Hadamard Hypervector Processing 
 for Efficient Hyperdimensional Computing
DESCRIPTION:Abu Kaiser Mohammad Masum (University of Louisiana, Lafayette)
 ; Shoushtari Moghadam (Case Western Reserve University); Sabrina Hassan Mo
 on and Ahmed Mamdouh (University of South Florida); M. Hassan Najafi (Case
  Western Reserve University); Dayane Reis (University of South Florida); a
 nd Sercan Aygun (University of Louisiana, Lafayette)\n\nInspired by the hu
 man brain, Hyperdimensional Computing (HDC) processes information efficien
 tly by operating in high-dimensional space using hypervectors. While previ
 ous works focus on optimizing pregenerated hypervectors in software, this 
 study introduces a novel on-the-fly vector generation method in hardware w
 ith O(1) complexity, compared to the O(N) iterative search used in convent
 ional approaches to find the best orthogonal hypervectors. Our approach le
 verages Hadamard binary coefficients and unary computing to simplify encod
 ing into addition-only operations after the generation stage in ASIC, impl
 emented using inmemory computing. The proposed design significantly improv
 es accuracy and computational efficiency across multiple benchmark dataset
 s.\n\n
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