SQuantizer: Simultaneous Learning for Both Sparse and Low-precision Neural Networks. (arXiv:1812.08301v1 [cs.CV])

Deep neural networks have achieved state-of-the-art accuracies in a wide
range of computer vision, speech recognition, and machine translation tasks.
However the limits of memory bandwidth and computational power constrain the
range of devices capable of deploying these modern networks. To address this
problem, we propose SQuantizer, a new training method that jointly optimizes
for both sparse and low-precision neural networks while maintaining high
accuracy and providing a high compression rate. This approach brings
sparsification and low-bit quantization into a single training pass, employing
these techniques in an order demonstrated to be optimal. Our method achieves
state-of-the-art accuracies using 4-bit and 2-bit precision for ResNet18,
MobileNet-v2 and ResNet50, even with high degree of sparsity. The compression
rates of 18x for ResNet18 and 17x for ResNet50, and 9x for MobileNet-v2 are
obtained when SQuantizing both weights and activations within 1% and 2% loss in
accuracy for ResNets and MobileNet-v2 respectively. An extension of these
techniques to object detection also demonstrates high accuracy on YOLO-v2.
Additionally, our method allows for fast single pass training, which is
important for rapid prototyping and neural architecture search techniques.
Finally extensive results from this simultaneous training approach allows us to
draw some useful insights into the relative merits of sparsity and

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