  Learn in-situ is a growing trend for Edge AI. Training deep neural network
(DNN) on edge devices is challenging because both energy and memory are
constrained. Low precision training helps to reduce the energy cost of a single
training iteration, but that does not necessarily translate to energy savings
for the whole training process, because low precision could slows down the
convergence rate. One evidence is that most works for low precision training
keep an fp32 copy of the model during training, which in turn imposes memory
requirements on edge devices. In this work we propose Adaptive Precision
Training. It is able to save both total training energy cost and memory usage
at the same time. We use model of the same precision for both forward and
backward pass in order to reduce memory usage for training. Through evaluating
the progress of training, APT allocates layer-wise precision dynamically so
that the model learns quicker for longer time. APT provides an application
specific hyper-parameter for users to play trade-off between training energy
cost, memory usage and accuracy. Experiment shows that APT achieves more than
50% saving on training energy and memory usage with limited accuracy loss. 20%
more savings of training energy and memory usage can be achieved in return for
a 1% sacrifice in accuracy loss.

Machine Learning