2026-03-08T03:54:01Z https://ipsj.ixsq.nii.ac.jp/oai

oai:ipsj.ixsq.nii.ac.jp:00217233 2025-01-19T15:34:23Z 1164:2822:10894:10895

Layer-wise power/performance modelling for single-board CNN inference Layer-wise power/performance modelling for single-board CNN inference Yi, Ng Kuan Aalaa, M.A. Babai Satoshi, Kawakami Teruo, Tanimoto Koji, Inoue Yi, Ng Kuan Aalaa, M.A. Babai Satoshi, Kawakami Teruo, Tanimoto Koji, Inoue ニューラルネットワーク Intermittent executions and energy harvesting technologies are promising candidates to enable renewable energy on small-scale computer systems like single-board computers, making sustainable computing possible. In this work, we implemented an energy consumption prediction framework for each layer of CNN executing on single-board computers based on NeuralPower as the first step towards enabling energy-efficient intermittent execution of CNN inference on single-board computers. We found that layer hyperparameters cannot explain all the variations in execution time and power consumption when the layer is executed. Model's prediction can be improved with the knowledge of performance counter values, but these values are not available before a layer is executed. Furthermore, our analysis revealed that implementation optimization like sparse matrix multiplication might cause a layer's execution time and power to change with its input values. Intermittent executions and energy harvesting technologies are promising candidates to enable renewable energy on small-scale computer systems like single-board computers, making sustainable computing possible. In this work, we implemented an energy consumption prediction framework for each layer of CNN executing on single-board computers based on NeuralPower as the first step towards enabling energy-efficient intermittent execution of CNN inference on single-board computers. We found that layer hyperparameters cannot explain all the variations in execution time and power consumption when the layer is executed. Model's prediction can be improved with the knowledge of performance counter values, but these values are not available before a layer is executed. Furthermore, our analysis revealed that implementation optimization like sparse matrix multiplication might cause a layer's execution time and power to change with its input values. 情報処理学会 2022-03-03 eng technical report https://ipsj.ixsq.nii.ac.jp/records/217233 2188-868X AA12149313 研究報告組込みシステム（EMB） 2022-EMB-59 13 1 11 https://ipsj.ixsq.nii.ac.jp/record/217233/files/IPSJ-EMB22059013.pdf application/pdf 20.9 MB 2024-03-03