ElasticTrainer: Speeding Up On-Device Training with Runtime Elastic
  Tensor Selection
Kai Huang Boyuan Yang Wei Gao
On-device training is essential for neural networks (NNs) to continuously
adapt to new online data, but can be time-consuming due to the device's limited
computing power. To speed up on-device training, existing schemes select
trainable NN portion offline or conduct unrecoverable selection at runtime, but
the evolution of trainable NN portion is constrained and cannot adapt to the
current need for training. Instead, runtime adaptation of on-device training
should be fully elastic, i.e., every NN substructure can be freely removed from
or added to the trainable NN portion at any time in training. In this paper, we
present ElasticTrainer, a new technique that enforces such elasticity to
achieve the required training speedup with the minimum NN accuracy loss.
Experiment results show that ElasticTrainer achieves up to 3.5x more training
speedup in wall-clock time and reduces energy consumption by 2x-3x more
compared to the existing schemes, without noticeable accuracy loss.