XNOR-Net is not currently part of TensorFlow, although there is a feature request to support it. [0] The main difference is that XNOR-Net uses 1 bit quantized

XNOR-Net is not currently part of TensorFlow, although there is a feature request to support it. [0] The main difference is that XNOR-Net uses 1 bit quantized weights and XNOR+popcount to approximate the dot product of convolution, which can be implemented very efficiently by using 64-bit arithmetic instructions on a CPU to operate over 64 1-bit components in parallel. [1] TensorFlow's optimization is also using discretized weights, but AFAIK targeted at 8-bit quantized weights. [2] [0] https://github.com/tensorflow/tensorflow/issues/1592 https://github.com/tensorflow/tensorflow/issues/1592 [1] https://arxiv.org/pdf/1603.05279v4.pdf https://arxiv.org/pdf/1603.05279v4.pdf [2] https://petewarden.com/2015/05/23/why-are-eight-bits-enough-for-deep-neural-networks/ https://petewarden.com/2015/05/23/why-are-eight-bits-enough-...