Delta-DNN: Efficiently Compressing Deep Neural Networks via Exploiting Floats Similarity
Zhenbo Hu, Xiangyu Zou, Wen Hua Xia, Sian Jin, Dingwen Tao, Yang Liu, Weizhe Zhang, Zheng Zhang · 2020
Deep neural networks (DNNs) have gained considerable attention in various real-world applications due to the strong performance on representation learning. However, a DNN needs to be trained many epochs for pursuing a higher inference accuracy, which requires storing sequential versions of DNNs and releasing the updated versions to users. As a result, large amounts of storage and network resources are required, significantly hampering DNN utilization on resource-constrained platforms (e.g., IoT, mobile phone).