Evaluating Performance, Power and Energy of Deep Neural Networks on CPUs and GPUs
Communications in Computer and Information Science, ISSN: 1865-0937, Vol: 1494 CCIS, Page: 196-221
2021
- 7Citations
- 15Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Deep learning has achieved accuracy and fast training speed and has been successfully applied to many fields, including speech recognition, text processing, image processing and video processing. However, the cost of high power and energy comes together with the high accuracy and training speed of Deep Neural Network (DNN). This inspires researchers to perform characterization in terms of performance, power and energy for guiding the architecture design of DNN models. There are three critical issues to solve for designing a both accurate and energy-efficient DNN model: i) how the software parameters affect the DNN models; ii) how the hardware parameters affect the DNN models; and iii) how to choose the best energy-efficient DNN model. To answer the three issues above, we capture and analyze the performance, power and energy behaviors for multiple experiment settings. We evaluate four DNN models (i.e., LeNet, GoogLeNet, AlexNet, and CaffeNet) with various parameter settings (both hardware and software) on both CPU and GPU platforms. Evaluation results provide detailed DNN characterization and some key insights to facilitate the design of energy-efficient deep learning solutions.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85119859358&origin=inward; http://dx.doi.org/10.1007/978-981-16-7443-3_12; https://link.springer.com/10.1007/978-981-16-7443-3_12; https://dx.doi.org/10.1007/978-981-16-7443-3_12; https://link.springer.com/chapter/10.1007/978-981-16-7443-3_12
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know