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DTSTAMP:20211207T055346Z
LOCATION:230-231-232
DTSTART;TZID=America/Chicago:20211116T153000
DTEND;TZID=America/Chicago:20211116T170000
UID:submissions.supercomputing.org_SC21_sess175@linklings.com
SUMMARY:Hardware Efficient Deep Learning
DESCRIPTION:Paper\n\nAPNN-TC: Accelerating Arbitrary Precision Neural Netw
 orks on Ampere GPU Tensor Cores\n\nFeng, Wang, Geng, Li, Ding\n\nOver the 
 years, accelerating neural networks with quantization has been widely stud
 ied. Unfortunately, prior efforts with diverse precisions (e.g., 1-bit wei
 ghts and 2-bit activations) are usually restricted by limited precision su
 pport on GPUs (e.g., int1 and int4). To break such restrictions, we i...\n
 \n---------------------\nEdge-Based Hyperdimensional Learning System with 
 Brain-Like Neural Adaptation\n\nZou, Kim, Imani, Alimohamadi, Cammarota...
 \n\nHyperdimensional Computing (HDC) is a brain-inspired learning approach
  for efficient and robust learning on today’s embedded devices. Encoding, 
 or transforming the input data into high-dimensional representation, is th
 e key first step of HDC before performing a learning task.  In this paper,
  we have...\n\n---------------------\nDr. Top-k: Delegate-Centric Top-k Co
 mputation on GPUs\n\nGaihre, Zheng, Weitze, Li, Song...\n\nRecent top-k co
 mputation efforts explore the possibility of revising various sorting algo
 rithms to answer top-k queries on GPUs. These endeavors, unfortunately, pe
 rform significantly more work than needed. This paper introduces Dr. Top-k
 , a Delegate-centric top-k system on GPUs that can reduce the t...\n\n\nTa
 g: Data Analytics, Machine Learning and Artificial Intelligence\n\nRegistr
 ation Category: Tech Program Reg Pass
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