PLiNIO: A User-Friendly Library of Gradient-based Methods for
  Complexity-aware DNN Optimization
Daniele Jahier Pagliari Matteo Risso Beatrice Alessandra Motetti Alessio Burrello
Accurate yet efficient Deep Neural Networks (DNNs) are in high demand,
especially for applications that require their execution on constrained edge
devices. Finding such DNNs in a reasonable time for new applications requires
automated optimization pipelines since the huge space of hyper-parameter
combinations is impossible to explore extensively by hand. In this work, we
propose PLiNIO, an open-source library implementing a comprehensive set of
state-of-the-art DNN design automation techniques, all based on lightweight
gradient-based optimization, under a unified and user-friendly interface. With
experiments on several edge-relevant tasks, we show that combining the various
optimizations available in PLiNIO leads to rich sets of solutions that
Pareto-dominate the considered baselines in terms of accuracy vs model size.
Noteworthy, PLiNIO achieves up to 94.34% memory reduction for a <1% accuracy
drop compared to a baseline architecture.