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Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark
Cody Coleman
,
Daniel Kang
, Deepak Narayanan
, Luigi Nardi
, Tian Zhao
, Jian Zhang
, Peter Bailis
, Kunle Olukotun
, Chris Ré
, Matei Zaharia
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Keyphrases
Unseen
100%
Performance Benchmark
100%
Machine Learning Performance
100%
Coefficient of Variation
50%
Model Accuracy
50%
Evaluation Criteria
50%
Hardware Complexity
50%
Optimization Algorithm
50%
Computational Performance
50%
Final Model
50%
Clock Frequency
50%
ImageNet
50%
Hardware Optimization
50%
Training Time
50%
Hardware Capabilities
50%
Deep Learning
50%
Industrial Group
50%
Training Procedure
50%
Reduced Precision
50%
Software Optimization
50%
Tensor Cores
50%
Computer Science
Performance Benchmark
100%
Learning Performance
100%
Machine Learning
100%
Learning System
100%
Computer Hardware
100%
Evaluation Criterion
50%
Coefficient of Variation
50%
Model Accuracy
50%
Graphics Processing Unit
50%
Deep Learning Method
50%