This paper provides an overview of efficient deep learning methods, systems and applications.

Pruning and Quantization for Transformer models such as BERT and GPT

HAT NAS framework leverages the hardware feedback in the neural architecture search loop, providing a most suitable model for the target hardware platform. The results on different hardware platforms and datasets show that HAT searched models have better accuracy-efficiency trade-offs.