  Low precision training can significantly reduce the computational overhead of
training deep neural networks (DNNs). Though many such techniques exist, cyclic
precision training (CPT), which dynamically adjusts precision throughout
training according to a cyclic schedule, achieves particularly impressive
improvements in training efficiency, while actually improving DNN performance.
Existing CPT implementations take common learning rate schedules (e.g.,
cyclical cosine schedules) and use them for low precision training without
adequate comparisons to alternative scheduling options. We define a diverse
suite of CPT schedules and analyze their performance across a variety of DNN
training regimes, some of which are unexplored in the low precision training
literature (e.g., node classification with graph neural networks). From these
experiments, we discover alternative CPT schedules that offer further
improvements in training efficiency and model performance, as well as derive a
set of best practices for choosing CPT schedules. Going further, we find that a
correlation exists between model performance and training cost, and that
changing the underlying CPT schedule can control the tradeoff between these two
variables. To explain the direct correlation between model performance and
training cost, we draw a connection between quantized training and critical
learning periods, suggesting that aggressive quantization is a form of learning
impairment that can permanently damage model performance.
