Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Optimal Policy for Deployment of Machine Learning Models on Energy-Bounded Systems
Authors: Seyed Iman Mirzadeh, Hassan Ghasemzadeh
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that on the Image Net dataset, we can achieve a 20% energy reduction with only 0.3% accuracy drop compared to Squeeze-and-Excitation Networks. By performing comprehensive experiments on different machine learning tasks, we show the performance gain of our proposed solution. |
| Researcher Affiliation | Academia | Seyed Iman Mirzadeh and Hassan Ghasemzadeh Washington State University, USA EMAIL |
| Pseudocode | No | The paper describes mathematical formulations and algorithms but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using several open-source libraries (e.g., scikit-learn, TensorFlow, CVXPY, PyTorch) but does not provide a link or explicit statement about releasing the source code for the methodology developed in this paper. |
| Open Datasets | Yes | We used the Human Activity Recognition Using Smartphones (UCI-HAR) dataset [Anguita et al., 2013]... The Image Net classification dataset [Russakovsky et al., 2014] has 1.28 million training images and 50,000 validation images that includes of 1000 classes. |
| Dataset Splits | Yes | The Image Net classification dataset [Russakovsky et al., 2014] has 1.28 million training images and 50,000 validation images that includes of 1000 classes. |
| Hardware Specification | Yes | To measure the power consumption of different machine learning models with different implementations, we utilized Intel s Running Average Power Limit (RAPL) [Weaver et al., 2012] implemented in the Likwid library [Center, 2019]. RAPL allows us to monitor energy consumption on the CPU chip and the attached DRAM. For a fair comparison, we used only a single core and fixed the clock frequency at 1.5GHz for all our experiments. |
| Software Dependencies | No | The paper mentions software like 'scikitlearn', 'Tensorflow library', 'CVXPY library' with 'ECOS solver', and 'Pytorch framework', but none of these mentions include specific version numbers. For example, 'scikitlearn [Pedregosa et al., 2011]' only provides a citation year, not a version. |
| Experiment Setup | Yes | For the classification task on this dataset, we used the objective introduced in (1) where K is set to 1000 inferences, λ = 0.1 K = 100, and ui is set to the constant value of 1 to penalize selecting many model. Both neural networks were trained using the Adam Optimizer [Kingma and Ba, 2014] with Tensorflow library for 50 epochs with early stopping. |