Benchmarking Contemporary Deep Learning Hardware and Frameworks: A Survey of Qualitative Metrics
- DOI
- 10.1109/cogmi48466.2019.00029
- Published
- 2019-12
- Container
- 2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)
- Publisher
- IEEE
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/cogmi48466.2019.00029,
title = {Benchmarking Contemporary Deep Learning Hardware and Frameworks: A Survey of Qualitative Metrics},
author = {Wei Dai and Daniel Berleant},
year = {2019},
journal = {2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)},
doi = {10.1109/cogmi48466.2019.00029},
url = {https://doi.org/10.1109/cogmi48466.2019.00029}
}RIS
TY - JOUR TI - Benchmarking Contemporary Deep Learning Hardware and Frameworks: A Survey of Qualitative Metrics AU - Wei Dai AU - Daniel Berleant PY - 2019 JO - 2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI) DO - 10.1109/cogmi48466.2019.00029 UR - https://doi.org/10.1109/cogmi48466.2019.00029 ER -
APA
Dai, W., & Berleant, D. (2019). Benchmarking Contemporary Deep Learning Hardware and Frameworks: A Survey of Qualitative Metrics. 2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI). https://doi.org/10.1109/cogmi48466.2019.00029
Source records
- crossref · retrieved 2026-09-25T12:23:12.439Z