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.

本文旨在通过实验在多种边缘任务中展示 PLiNIO 的各种优化方法在准确性和模型大小方面较基线架构具有显著优势，最多可以实现 94.34% 的内存缩减且准确率下降不超过 1%。