MingSun-Tse/Efficient-Deep-Learning
Collection of recent methods on (deep) neural network compression and acceleration.
This project helps machine learning engineers and researchers optimize deep neural networks for deployment in resource-constrained environments like mobile phones or embedded devices. It provides a curated collection of techniques for making existing neural network models smaller and faster. You provide a trained deep neural network, and this project offers methods to reduce its size and computational requirements while maintaining accuracy.
954 stars. No commits in the last 6 months.
Use this if you need to deploy large deep learning models on hardware with limited memory, processing power, or battery life, and you want to reduce their footprint and increase inference speed.
Not ideal if you are looking for methods to initially design neural network architectures from scratch or optimize models for maximal accuracy without concern for computational efficiency.
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MIT
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Last pushed
Apr 04, 2025
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