Title: Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy,
Abstract: Deep learning networks have achieved state-of-the-art accuracies on computer
vision workloads like image classification and object detection. The performant
systems, however, typically involve big models with numerous parameters. Once
trained, a challenging aspect for such top performing models is deployment on
resource constrained inference systems - the models (often deep networks or
wide networks or both) are compute and memory intensive. Low-precision numerics
and model compression using knowledge distillation are popular techniques to
lower both the compute requirements and memory footprint of these deployed
models. In this paper, we study the combination of these two techniques and
show that the performance of low-precision networks can be significantly
improved by using knowledge distillation techniques. Our approach, Apprentice,
achieves state-of-the-art accuracies using ternary precision and 4-bit
precision for variants of ResNet architecture on ImageNet dataset. We present
three schemes using which one can apply knowledge distillation techniques to
various stages of the train-and-deploy pipeline.