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DTSTAMP:20210402T160551Z
LOCATION:Track 4
DTSTART;TZID=America/New_York:20201117T100000
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UID:submissions.supercomputing.org_SC20_sess175@linklings.com
SUMMARY:Sparsity in Deep Learning
DESCRIPTION:Paper\n\nAccelerating Sparse DNN Models without Hardware-Suppo
 rt via Tile-Wise Sparsity\n\nGuo, Hsueh, Leng, Qiu, Guan...\n\nNetwork pru
 ning can reduce the high computational cost of deep neural network (DNN) m
 odels.  To maintain their accuracies, however, sparse models often carry r
 andomly-distributed weights, leading to irregular computations. Consequent
 ly, sparse models cannot achieve meaningful speedup on commodity ha...\n\n
 ---------------------\nSparse GPU Kernels for Deep Learning\n\nGale, Zahar
 ia, Young, Elsen\n\nScientific workloads have traditionally exploited high
  levels of sparsity to accelerate computation and reduce memory requiremen
 ts. While deep neural networks can be made sparse, achieving practical spe
 edups on GPUs is difficult because these applications have relatively mode
 rate levels of sparsity ...\n\n---------------------\nSpTFS: Sparse Tensor
  Format Selection for MTTKRP via Deep Learning\n\nSun, Liu, Dun, Yang, Lua
 n...\n\nCanonical polyadic decomposition (CPD) is one of the most common t
 ensor computations adopted in many scientific applications. The major perf
 ormance bottleneck of CPD is matricized tensor times Katri-Rao product (MT
 TKRP). To optimize the performance of MTTKRP, various sparse tensor format
 s have been ...\n\n\nTag: Accelerators, FPGA, and GPUs, Machine Learning, 
 Deep Learning and Artificial Intelligence, Sparse Computation\n\nRegistrat
 ion Category: Tech Program Reg Pass
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