Nov 30, 2018 · Network compression reduces the computational complexity and memory consumption of deep neural networks by reducing the number of parameters.
Network compression reduces the computational com- plexity and memory consumption of deep neural networks by reducing the number of parameters.
Jun 10, 2024 · Abstract:This paper explores the combination of neural network quantization and entropy coding for memory footprint minimization.
The proposed method considers the whole network to choose the right rank configuration which satisfies the constraints on FLOPs and memory while maintaining ...
Jul 8, 2022 · Experimental results demonstrate that NNCS can significantly outperform the other existing state-of-the-art methods in terms of parameter ...
A method called WoodFisher to compute a faithful and efficient estimate of the inverse Hessian. Our main application is to neural network compression.
Nov 6, 2020 · The goal of model compression in deep learning is to construct a smaller network with similar or better performance than a larger deep network ( ...
Abstract—Traditional neural network compression (NNC) methods decrease the model size and floating-point opera- tions (FLOPs) in the manner of screening out ...
Jun 18, 2023 · This tutorial will introduce effective methodologies for re-designing algorithms for efficient content understanding, image generation, and neural rendering.
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To the best of our knowledge, we are the first to show that accuracy recovery from a compressed network can be done with few unlabeled samples within minutes.