  Model quantization is leveraged to reduce the memory consumption and the
computation time of deep neural networks. This is achieved by representing
weights and activations with a lower bit resolution when compared to their high
precision floating point counterparts. The suitable level of quantization is
directly related to the model performance. Lowering the quantization precision
(e.g. 2 bits), reduces the amount of memory required to store model parameters
and the amount of logic required to implement computational blocks, which
contributes to reducing the power consumption of the entire system. These
benefits typically come at the cost of reduced accuracy. The main challenge is
to quantize a network as much as possible, while maintaining the performance
accuracy. In this work, we present a quantization method for the U-Net
architecture, a popular model in medical image segmentation. We then apply our
quantization algorithm to three datasets: (1) the Spinal Cord Gray Matter
Segmentation (GM), (2) the ISBI challenge for segmentation of neuronal
structures in Electron Microscopic (EM), and (3) the public National Institute
of Health (NIH) dataset for pancreas segmentation in abdominal CT scans. The
reported results demonstrate that with only 4 bits for weights and 6 bits for
activations, we obtain 8 fold reduction in memory requirements while loosing
only 2.21%, 0.57% and 2.09% dice overlap score for EM, GM and NIH datasets
respectively. Our fixed point quantization provides a flexible trade off
between accuracy and memory requirement which is not provided by previous
quantization methods for U-Net such as TernaryNet.
