Project partner: Amazon AWS Machine Learning, MXNet
In recent years, deep learning technologies achieved excellent performance and many breakthroughs in both academia and industry. However the state-of-the-art deep models are computational expensive
and consume large storage space. Deep learning is also strongly demanded by numerous applications from areas such as mobile platforms, wearable devices, autonomous robots and IoT devices. How to efficiently apply deep models on such low power devices becomes a challenging research problem. In this project we will explore several different approaches such as binarized, quantized as well as lightweight deep neural networks for this problem. The development is based on Apache MXNet which is a high performance and modular open source deep learning library developed by DMLC community. As a in progress research result we developed BMXNet which is an open source binary neural network implementation based on MXNet.