Evaluating the energy impact of device parameters for DNN inference on edge

Author:

Dutt Anurag1ORCID,Rachuri Sri Pramodh2ORCID,Lobo Ashley2ORCID,Shaik Nazeer2ORCID,Gandhi Anshul2ORCID,Liu Zhenhua2ORCID

Affiliation:

1. Computer Science, Stony Brook University, USA

2. Stony Brook University, USA

Funder

NSF (National Science Foundation)

Publisher

ACM

Reference16 articles.

1. DeepEdgeBench: Benchmarking Deep Neural Networks on Edge Devices

2. Linux Foundation. 2020. Sharpening the edge: Overview of the LF edge taxonomy and framework. https://www.lfedge.org/wp-content/uploads/2020/07/LFedge_Whitepaper.pdf

3. Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2015. Deep Residual Learning for Image Recognition. arxiv:1512.03385 [cs.CV]

4. Profiling Energy Consumption of Deep Neural Networks on NVIDIA Jetson Nano

5. Efficient-Grad: Efficient Training Deep Convolutional Neural Networks on Edge Devices with Grad ient Optimizations

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. PowerTrain: Fast, generalizable time and power prediction models to optimize DNN training on accelerated edges;Future Generation Computer Systems;2024-12

2. Empirical Evaluation of ML Models for Per-Job Power Prediction;Companion of the 15th ACM/SPEC International Conference on Performance Engineering;2024-05-07

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