  We propose a method of training quantization thresholds (TQT) for uniform
symmetric quantizers using standard backpropagation and gradient descent.
Contrary to prior work, we show that a careful analysis of the straight-through
estimator for threshold gradients allows for a natural range-precision
trade-off leading to better optima. Our quantizers are constrained to use
power-of-2 scale-factors and per-tensor scaling of weights and activations to
make it amenable for hardware implementations. We present analytical support
for the general robustness of our methods and empirically validate them on
various CNNs for ImageNet classification. We are able to achieve
near-floating-point accuracy on traditionally difficult networks such as
MobileNets with less than 5 epochs of quantized (8-bit) retraining. Finally, we
present Graffitist, a framework that enables automatic quantization of
TensorFlow graphs for TQT (available at https://github.com/Xilinx/graffitist ).
