Binary Convolution Network for faster real-time processing in ASICs

Tensorflow implementation of Towards Accurate Binary Convolutional Neural Network by Xiaofan Lin, Cong Zhao, and Wei Pan.
Why this network? Let's quote the authors

It has been known that using binary weights and activations drastically reduce memory size and accesses, and can replace arithmetic operations with more efficient bitwise operations, leading to much faster test-time inference and lower power consumption.
The implementation of the resulting binary CNN, denoted as ABC-Net, is shown to achieve much closer performance to its full-precision counterpart, and even reach the comparable prediction accuracy on ImageNet and forest trail datasets, given adequate binary weight bases and activations.

pip install -r requirements.txt

By default tensorflow-gpu will be installed. Make sure to have CUDA properly setup.

• ABC - Contains the original implementation of the ABC network
• ABC-layer-inference-support - Slightly modified functions for better inference time support (tl;dr moved the alpha training operation out of the layer)

• MNIST - Accuracy on validation set reached upto 94%. (Check the notebook for information)
• ImageNet - To be added

NOTE: shift_parameters and beta values are currently not trainable. This is because the gradient for tf.sign and tf.clip_by_value were not implemented in tensorflow v1.4. Even in the current version (tensorflow v1.8) the gradient for tf.sign is not implemented. Implementation of custom Straight Through Estimator (STE) is required.

• Test on ImageNet (2012)
• Add visualization of the complete ABC layer
• Port to tensorflow v1.8.0
• Implement custom STE for tf.sign