Optimal Policy for Deployment of Machine Learning Models on Energy-Bounded Systems
Authors: Seyed Iman Mirzadeh, Hassan Ghasemzadeh
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | 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 {seyediman.mirzadeh, hassan.ghasemzadeh}@wsu.edu |
| 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. |