MixSize training makes ImageNet classifiers resilient to smaller test images, matching baseline top-1 accuracy at 160x160 with about half the inference compute, while optionally improving accuracy or training speed.
Faster Neural Network Training with Data Echoing
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
In the twilight of Moore's law, GPUs and other specialized hardware accelerators have dramatically sped up neural network training. However, earlier stages of the training pipeline, such as disk I/O and data preprocessing, do not run on accelerators. As accelerators continue to improve, these earlier stages will increasingly become the bottleneck. In this paper, we introduce "data echoing," which reduces the total computation used by earlier pipeline stages and speeds up training whenever computation upstream from accelerators dominates the training time. Data echoing reuses (or "echoes") intermediate outputs from earlier pipeline stages in order to reclaim idle capacity. We investigate the behavior of different data echoing algorithms on various workloads, for various amounts of echoing, and for various batch sizes. We find that in all settings, at least one data echoing algorithm can match the baseline's predictive performance using less upstream computation. We measured a factor of 3.25 decrease in wall-clock time for ResNet-50 on ImageNet when reading training data over a network.
fields
cs.CV 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Mix & Match: training convnets with mixed image sizes for improved accuracy, speed and scale resiliency
MixSize training makes ImageNet classifiers resilient to smaller test images, matching baseline top-1 accuracy at 160x160 with about half the inference compute, while optionally improving accuracy or training speed.