  Binary Neural Networks (BNNs) can drastically reduce memory size and accesses
by applying bit-wise operations instead of standard arithmetic operations.
Therefore it could significantly improve the efficiency and lower the energy
consumption at runtime, which enables the application of state-of-the-art deep
learning models on low power devices. BMXNet is an open-source BNN library
based on MXNet, which supports both XNOR-Networks and Quantized Neural
Networks. The developed BNN layers can be seamlessly applied with other
standard library components and work in both GPU and CPU mode. BMXNet is
maintained and developed by the multimedia research group at Hasso Plattner
Institute and released under Apache license. Extensive experiments validate the
efficiency and effectiveness of our implementation. The BMXNet library, several
sample projects, and a collection of pre-trained binary deep models are
available for download at https://github.com/hpi-xnor
