skip to main content
10.1145/3123266.3129393acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
research-article

BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet

Published: 19 October 2017 Publication History

Abstract

Binary Neural Networks (BNNs) can drastically reduce memory size and accesses by applying bit-wise operations instead of standard arithmetic operations. Therefore it could significantly improve the efficiency and lower the energy consumption at runtime, which enables the application of state-of-the-art deep learning models on low power devices. BMXNet is an open-source BNN library based on MXNet, which supports both XNOR-Networks and Quantized Neural Networks. The developed BNN layers can be seamlessly applied with other standard library components and work in both GPU and CPU mode. BMXNet is maintained and developed by the multimedia research group at Hasso Plattner Institute and released under Apache license. Extensive experiments validate the efficiency and effectiveness of our implementation. The BMXNet library, several sample projects, and a collection of pre-trained binary deep models are available for download at https://github.com/hpi-xnor.

References

[1]
Renzo Andri, Lukas Cavigelli, Davide Rossi, and Luca Benini. 2016. YodaNN: An ultra-low power convolutional neural network accelerator based on binary weights VLSI (ISVLSI), 2016 IEEE Computer Society Annual Symposium on. IEEE, 236--241.
[2]
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. 2015. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. CoRR Vol. abs/1512.01274 (2015).
[3]
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David. 2015. BinaryConnect: Training Deep Neural Networks with binary weights during propagations Advances in Neural Information Processing Systems 28. 3123--3131.
[4]
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. 2009. ImageNet: A Large-Scale Hierarchical Image Database CVPR09.
[5]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770--778.
[6]
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. 2016. Binarized Neural Networks. In Advances in Neural Information Processing Systems 29. 4107--4115.
[7]
Google Inc. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. (2015). http://tensorflow.org/ Software available from tensorflow.org.
[8]
Yangqing Jia. 2014. Learning Semantic Image Representations at a Large Scale. Ph.D. Dissertation. bibinfoschoolEECS Department, University of California, Berkeley.
[9]
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014. Caffe: Convolutional Architecture for Fast Feature Embedding. arXiv preprint arXiv:1408.5093 (2014).
[10]
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. 2014. CIFAR-10 (Canadian Institute for Advanced Research). (2014).
[11]
Yann LeCun and Corinna Cortes. 2010. MNIST handwritten digit database. (2010). http://yann.lecun.com/exdb/mnist/
[12]
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi. 2016. XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks Computer Vision - ECCV 2016. 525--542.
[13]
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1--9.
[14]
Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou. 2016. DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. arXiv:1606.06160 {cs} (2016).

Cited By

View all
  • (2024)An encoding framework for binarized images using hyperdimensional computingFrontiers in Big Data10.3389/fdata.2024.13715187Online publication date: 14-Jun-2024
  • (2024)SI-BiViT: Binarizing Vision Transformers with Spatial InteractionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680872(8169-8178)Online publication date: 28-Oct-2024
  • (2024)GraphBinMatch: Graph-Based Similarity Learning for Cross-Language Binary and Source Code Matching2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW63119.2024.00103(506-515)Online publication date: 27-May-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
MM '17: Proceedings of the 25th ACM international conference on Multimedia
October 2017
2028 pages
ISBN:9781450349062
DOI:10.1145/3123266
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 October 2017

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. binary neural networks
  2. computer vision
  3. machine learning
  4. open source

Qualifiers

  • Research-article

Conference

MM '17
Sponsor:
MM '17: ACM Multimedia Conference
October 23 - 27, 2017
California, Mountain View, USA

Acceptance Rates

MM '17 Paper Acceptance Rate 189 of 684 submissions, 28%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)33
  • Downloads (Last 6 weeks)3
Reflects downloads up to 22 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)An encoding framework for binarized images using hyperdimensional computingFrontiers in Big Data10.3389/fdata.2024.13715187Online publication date: 14-Jun-2024
  • (2024)SI-BiViT: Binarizing Vision Transformers with Spatial InteractionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680872(8169-8178)Online publication date: 28-Oct-2024
  • (2024)GraphBinMatch: Graph-Based Similarity Learning for Cross-Language Binary and Source Code Matching2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW63119.2024.00103(506-515)Online publication date: 27-May-2024
  • (2024)ZOBNN: Zero-Overhead Dependable Design of Binary Neural Networks with Deliberately Quantized Parameters2024 IEEE 30th International Symposium on On-Line Testing and Robust System Design (IOLTS)10.1109/IOLTS60994.2024.10616090(1-7)Online publication date: 3-Jul-2024
  • (2024)Binary Neural NetworksNeural Networks with Model Compression10.1007/978-981-99-5068-3_2(7-48)Online publication date: 5-Feb-2024
  • (2023)LAB: Learnable Activation Binarizer for Binary Neural Networks2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV56688.2023.00636(6414-6423)Online publication date: Jan-2023
  • (2023)Efficient Memristive Binary Neural Networks With Spatial Separable Convolutions2023 International Conference on Neuromorphic Computing (ICNC)10.1109/ICNC59488.2023.10462808(225-234)Online publication date: 15-Dec-2023
  • (2023)A Systematic Literature Review on Binary Neural NetworksIEEE Access10.1109/ACCESS.2023.325836011(27546-27578)Online publication date: 2023
  • (2023)Eye diseases detection using deep learning with BAM attention moduleMultimedia Tools and Applications10.1007/s11042-023-17839-983:20(59061-59084)Online publication date: 27-Dec-2023
  • (2023)A comprehensive review of Binary Neural NetworkArtificial Intelligence Review10.1007/s10462-023-10464-w56:11(12949-13013)Online publication date: 30-Mar-2023
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media