arnabsanyal/lnsdnn
https://arxiv.org/abs/1910.09876
This project helps developers and researchers train deep neural networks more efficiently on devices with limited computational power. It takes standard neural network models and training datasets as input, and outputs trained models that perform similarly to traditional floating-point models but require significantly less processing power. This is ideal for those creating or deploying AI on edge devices.
No commits in the last 6 months.
Use this if you need to train or deploy deep neural networks on resource-constrained hardware, such as embedded systems or IoT devices, and want to reduce computational complexity while maintaining high accuracy.
Not ideal if you are primarily working with high-performance computing environments where computational resources are abundant and maximizing training speed or precision is paramount over resource efficiency.
Stars
7
Forks
3
Language
Python
License
MIT
Category
Last pushed
Jan 29, 2020
Commits (30d)
0
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