DjiNN provides Deep Neural Networks (DNN) as a service and Tonic Suite is a suite of 7 applications that use the service. Tonic Suite includes image, speech, and natural language processing applications that use a common DNN backend as their machine learning component.
DjiNN and Tonic is developed by Clarity Lab at the University of Michigan. DjiNN is published at the International Symposium on Computer Architecture (ISCA) 2015. Link to the publication [1].

 

If you’re interested in contributing to DjiNN, fork our repo or post to djinn-users.

Contributors

DjiNN and Tonic is developed by Clarity Lab under the supervision of Lingjia Tang and Jason Mars. The members of Clarity Lab and its collaborators who have contributed are listed on the people page.

[1] [pdf] Johann Hauswald, Yiping Kang, Michael A. Laurenzano, Quan Chen, Cheng Li, Ronald Dreslinski, Trevor Mudge, Jason Mars, and Lingjia Tang. Djinn and Tonic: DNN as a Service and Its Implications for Future Warehouse Scale Computers. In Proceedings of the 42nd Annual International Symposium on Computer Architecture (ISCA), ISCA ’15, New York, NY, USA, 2015. ACM. Acceptance Rate: 19%
[Bibtex]
@inproceedings{hauswald15isca,
author = {Hauswald, Johann and Kang, Yiping and Laurenzano, Michael A. and Chen, Quan and Li, Cheng and Dreslinski, Ronald and Mudge, Trevor and Mars, Jason and Tang, Lingjia},
title = {Djinn and Tonic: DNN as a Service and Its Implications for Future Warehouse Scale Computers},
booktitle = {Proceedings of the 42nd Annual International Symposium on Computer Architecture (ISCA)},
series = {ISCA '15},
year = {2015},
location = {Istanbul, Turkey},
publisher = {ACM},
address = {New York, NY, USA},
note = {Acceptance Rate: 19%},
}