A Greedy Algorithm for Quantizing Neural Networks

Eric Lybrand; Rayan Saab

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arXiv · 2010.15979

A Greedy Algorithm for Quantizing Neural Networks

Eric Lybrand·Rayan Saab

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Abstract

We propose a new computationally efficient method for quantizing the weights of pre- trained neural networks that is general enough to handle both multi-layer perceptrons and convolutional neural networks. Our method deterministically quantizes layers in an iterative fashion with no complicated re-training required. Specifically, we quantize each neuron, or hidden unit, using a greedy path-following algorithm. This simple algorithm is equivalent to running a dynamical system, which we prove is stable for quantizing a single-layer neural network (or, alternatively, for quantizing the first layer of a multi-layer network) when the training data are Gaussian. We show that under these assumptions, the quantization error decays with the width of the layer, i.e., its level of over-parametrization. We provide numerical experiments, on multi-layer networks, to illustrate the performance of our methods on MNIST and CIFAR10 data, as well as for quantizing the VGG16 network using ImageNet data.

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Eric Lybrand, Rayan Saab. 2020-10-29. A Greedy Algorithm for Quantizing Neural Networks. https://arxiv.org/abs/2010.15979

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