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Guaranteed Quantization Error Computation for Neural Network Model Compression

arXiv 2304.13812 ↗First posted 26 Apr 2023cs.LG cs.AI cs.NEcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 17 references that are in the map. First posted on arXiv 26 Apr 2023.

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References · 13

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1 more
  1. 2020Resource-Efficient Neural Networks for Embedded Systemscited by 20

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