Guaranteed Quantization Error Computation for Neural Network Model Compression

• URL: http://arxiv.org/abs/2304.13812v1
• Date: Wed, 26 Apr 2023 20:21:54 GMT
• Title: Guaranteed Quantization Error Computation for Neural Network Model Compression
• Authors: Wesley Cooke, Zihao Mo, Weiming Xiang
• Abstract summary: Neural network model compression techniques can address the computation issue of deep neural networks on embedded devices in industrial systems. A merged neural network is built from a feedforward neural network and its quantized version to produce the exact output difference between two neural networks.
• Score: 2.610470075814367
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural network model compression techniques can address the computation issue of deep neural networks on embedded devices in industrial systems. The guaranteed output error computation problem for neural network compression with quantization is addressed in this paper. A merged neural network is built from a feedforward neural network and its quantized version to produce the exact output difference between two neural networks. Then, optimization-based methods and reachability analysis methods are applied to the merged neural network to compute the guaranteed quantization error. Finally, a numerical example is proposed to validate the applicability and effectiveness of the proposed approach.

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