A Greedy Algorithm for Quantizing Neural Networks

• URL: http://arxiv.org/abs/2010.15979v2
• Date: Sun, 15 Aug 2021 04:42:45 GMT
• Title: A Greedy Algorithm for Quantizing Neural Networks
• Authors: Eric Lybrand, Rayan Saab
• Abstract summary: We propose a new computationally efficient method for quantizing the weights of pre- trained neural networks. Our method deterministically quantizes layers in an iterative fashion with no complicated re-training required.
• Score: 4.683806391173103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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