  A pruning-based AutoML framework for run-time reconfigurability, namely RT3,
is proposed in this work. This enables Transformer-based large Natural Language
Processing (NLP) models to be efficiently executed on resource-constrained
mobile devices and reconfigured (i.e., switching models for dynamic hardware
conditions) at run-time. Such reconfigurability is the key to save energy for
battery-powered mobile devices, which widely use dynamic voltage and frequency
scaling (DVFS) technique for hardware reconfiguration to prolong battery life.
In this work, we creatively explore a hybrid block-structured pruning (BP) and
pattern pruning (PP) for Transformer-based models and first attempt to combine
hardware and software reconfiguration to maximally save energy for
battery-powered mobile devices. Specifically, RT3 integrates two-level
optimizations: First, it utilizes an efficient BP as the first-step compression
for resource-constrained mobile devices; then, RT3 heuristically generates a
shrunken search space based on the first level optimization and searches
multiple pattern sets with diverse sparsity for PP via reinforcement learning
to support lightweight software reconfiguration, which corresponds to available
frequency levels of DVFS (i.e., hardware reconfiguration). At run-time, RT3 can
switch the lightweight pattern sets within 45ms to guarantee the required
real-time constraint at different frequency levels. Results further show that
RT3 can prolong battery life over 4x improvement with less than 1% accuracy
loss for Transformer and 1.5% score decrease for DistilBERT.
