Ristretto is an automated framework for the quantization and compression of Convolutional Neural Networks (CNNs).
The primary goal of this project is to enable fast and energy-efficient inference of artificial intelligence models on resource-constrained hardware platforms, such as FPGAs and custom accelerators. By reducing the bit-width of network parameters and activations, Ristretto significantly lowers the memory footprint and computational requirements of deep learning models with minimal impact on accuracy.
- Hardware-Oriented Approximation: Automatically simulates the effects of reduced precision on CNN performance.
- Mixed-Precision Quantization: Supports different bit-widths for different layers to optimize the trade-off between speed and accuracy.
- Dynamic Fixed-Point: Uses a flexible fixed-point format that adapts to the dynamic range of different network components.
- Automated Fine-Tuning: Provides tools to retrain quantized networks to recover accuracy lost during the approximation process.
Ristretto was developed by Philipp Gysel during his Master's thesis at UC Davis. The open-source project was introduced alongside a workshop paper at ICLR titled Hardware-oriented Approximation of Convolutional Neural Networks.
Ristretto is a fork of the Caffe deep learning framework. It extends the original Caffe implementation by introducing specialized layers and scoring functions designed for network approximation and hardware-friendly deployment.
If you find this open-source project useful for your own research, please consider citing this paper:
@article{gysel2018ristretto,
title={Ristretto: A Framework for Empirical Study of Resource-Efficient Inference in Convolutional Neural Networks},
author={Gysel, Philipp and Pimentel, Jon and Motamedi, Mohammad and Ghiasi, Soheil},
journal={IEEE Transactions on Neural Networks and Learning Systems},
volume={29},
number={11},
pages={5784--5789},
year={2018},
publisher={IEEE},
doi={10.1109/TNNLS.2018.2808319}
}-
Ristretto webpage: Please check out the official Ristretto webpage for more information. The official webpage contains usage examples and comprehensive walkthrough guides.
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Ristretto forum: If you have any technical questions about Ristretto, please use the Google Group.
As mentioned, Ristretto is based on Caffe, a deep learning framework created by Berkeley AI Research (BAIR) and community contributors. Its original author is Yangqing Jia. Below you can find the licensing and citation instructions from Caffe.
Caffe is released under the BSD 2-Clause license.
Please cite Caffe in your publications if it helps your research:
@article{jia2014caffe,
Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
Journal = {arXiv preprint arXiv:1408.5093},
Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
Year = {2014}
}