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DTSTAMP:20230712T175214Z
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DTSTART;TZID=America/Los_Angeles:20230712T155500
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UID:dac_DAC 2023_sess126_RESEARCH1553@linklings.com
SUMMARY:Cryogenic In-Memory Matrix-Vector Multiplication using Ferroelectr
 ic Superconducting Quantum Interference Device (FE-SQUID)
DESCRIPTION:Research Manuscript\n\nShamiul Alam and Jack Hutchins (Univers
 ity of Tennessee, Knoxville); Md Shafayat Hossain (Princeton University); 
 Kai Ni (Rochester Institute of Technology); Vijaykrishnan Narayanan (Penns
 ylvania State University); and Ahmedullah Aziz (University of Tennessee, K
 noxville)\n\nCryogenic in-memory implementation of machine learning (ML) a
 lgorithms can be a game-changer for quantum computing systems. The Ferroel
 ectric Superconducting Quantum Interference Device (FE-SQUID) is promising
  as a memory element, thanks to its non-volatile nature, voltage-controlle
 d switching, separate read-write paths, and scalability. Here, we demonstr
 ate matrix-vector multiplication (MVM) using FE-SQUID-based cryogenic memo
 ry for energy-efficient implementation of ML algorithms. FE-SQUID-based me
 mory allows optimizing the read operation for in-memory computing without 
 imposing any limits on memory performance. We utilize this FE-SQUID-based 
 in-memory MVM to perform a handwritten digit recognition task and achieve 
 an average accuracy of 95% with the MNIST handwritten digit dataset.\n\nTo
 pic: Design\n\nKeyword: Emerging Device Technologies\n\nSession Chairs: Ka
 i Ni (Rochester Institute of Technology) and Michael Niemier (University o
 f Notre Dame)
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