In this work we present a new framework for neural networks compression with
fine-tuning, which we called Neural Network Compression Framework (NNCF). It
leverages recent advances of various network compression methods and implements
some of them, such as sparsity, quantization, and binarization. These methods
allow getting more hardware-friendly models which can be efficiently run on
general-purpose hardware computation units (CPU, GPU) or special Deep Learning
accelerators. We show that the developed methods can be successfully applied to
a wide range of models to accelerate the inference time while keeping the
original accuracy. The framework can be used within the training samples, which
are supplied with it, or as a standalone package that can be seamlessly
integrated into the existing training code with minimal adaptations. Currently,
a PyTorch version of NNCF is available as a part of OpenVINO Training
Extensions at this https URL

该论文提出了一个新的神经网络压缩框架 NNCF，它基于当前各种网络压缩方法进行改进，如稀疏化、量化和二值化。这些方法可以加速推理时间，同时保持原有的准确性。NNCF 可作为训练样本或单独的软件包供使用，并且可以无缝地集成到现有的训练代码中。