QD-Compressor: a Quantization-based Delta Compression Framework for Deep Neural Networks

Deep neural networks (DNNs) have achieved remarkable success in many fields. Large-scale DNNs also bring storage challenges when storing snapshots for preventing clusters' frequent failures, and bring massive internet traffic when dispatching or updating DNNs for resource-constrained devices (e.g., IoT devices, mobile phones). Several approaches are aiming to compress DNNs. The Recent work, Delta-DNN, notices high similarity existed in DNNs and thus calculates differences between them for improving the compression ratio. However, we observe that Delta-DNN, applying traditional global lossy quantization technique in calculating differences of two neighboring versions of the DNNs, can not fully exploit the data similarity between them for delta compression. This is because the parameters' value ranges (and also the delta data in Delta-DNN) are varying among layers in DNNs, which inspires us to propose a local-sensitive quantization scheme: the quantizers are adaptive to parameters' local value ranges in layers. Moreover, instead of quantizing differences of DNNs in Delta-DNN, our approach quantizes DNNs before calculating differences to make the differences more compressible. Besides, we also propose an error feedback mechanism to reduce DNNs' accuracy loss caused by the lossy quantization. Therefore, we design a novel quantization-based delta compressor called QD-Compressor, which calculates the lossy differences between epochs of DNNs for saving storage cost of backing up DNNs' snapshots and internet traffic of dispatching DNNs for resource-constrained devices. Experiments on several popular DNNs and datasets show that QD-Compressor obtains a compression ratio of 2.4x similar to 31.5x higher than the state-of-the-art approaches while well maintaining the model's test accuracy.