Evaluating the energy impact of device parameters for DNN inference on edge
Author:
Affiliation:
1. Computer Science, Stony Brook University, USA
2. Stony Brook University, USA
Funder
NSF (National Science Foundation)
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3634769.3634809
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
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