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LOCATION:Track 4
DTSTART;TZID=America/New_York:20201119T130000
DTEND;TZID=America/New_York:20201119T143000
UID:submissions.supercomputing.org_SC20_sess178@linklings.com
SUMMARY:Quantized and Factorized Deep Learning
DESCRIPTION:Paper\n\nConvolutional Neural Network Training with Distribute
 d K-FAC\n\nPauloski, Zhang, Huang, Xu, Foster\n\nTraining neural networks 
 with many processors can reduce time-to-solution; it is challenging, howev
 er, to maintain convergence and efficiency at large scales. The Kronecker-
 factored Approximate Curvature (K-FAC) was recently proposed as an approxi
 mation of the Fisher Information Matrix that can be u...\n\n--------------
 -------\nBiQGEMM: Matrix Multiplication with Lookup Table For Binary-Codin
 g-Based Quantized DNNs\n\nJeon, Park, Kwon, Kim, Yun...\n\nThe number of p
 arameters in deep neural networks (DNNs) is rapidly increasing to support 
 complicated tasks and to improve model accuracy. Correspondingly, the amou
 nt of computations and required memory footprint increase as well. Quantiz
 ation is an efficient method to address such concerns. Unfortun...\n\n----
 -----------------\nTerm Quantization: Furthering Quantization at Run Time\
 n\nKung, McDanel, Zhang\n\nWe present a novel technique, called Term Quant
 ization (TQ), for furthering quantization at run time for improved computa
 tional efficiency of deep neural networks (DNNs) already quantized with co
 nventional quantization methods. TQ operates on power-of-two terms in expr
 essions of values. In computing...\n\n\nTag: Data Analytics, Compression, 
 and Management, Linear Algebra, Machine Learning, Deep Learning and Artifi
 cial Intelligence\n\nRegistration Category: Tech Program Reg Pass
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