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

BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet