Efficient Distributed DNN Training and Inference

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In recent years, deep learning has attracted tremendous attention in the machine learning community and beyond by achieving notable success across a wide spectrum of tasks such as computer vision, machinetranslationand speech recognition. Training these models, however, take days to weeks or sometimes even months to finish because of the high degree of computational complexity, large number of parameters andlarge datasets iteratively processed. This high computation cost necessitates distributed training to keep the training time reasonable and also require powerful hardware accelerators such as General Purpose GPUs (graphics processing units) and TPUs (tensor processing units).In this project we aim to investigate several major ways to improve the performance and energy efficiency of DNN training and inference through: (i) data encoding and compression in training; (ii) data encoding and compression in inference; (iii) networking efficiency improvements in distributed DNN training; (iv) training parallelism model research. We will also going to explore the possibility of using domain-specific ML accleration using Huawei NPU training cluster.