Prediction-Based Asynchronous CPU-Budget Allocation for Soft-Real-Time Applications

Ahmed, Safayet; Ferri, Bonnie

doi:10.1109/tc.2013.108

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…This is the policy used by the default Linux on-demand governor: it measures the idle time of the CPU and increases or decreases the CPU P-State in order to reach its target [19,20]. There are more sophisticated strategies for controlling DVFS including periodic workload scheduling [21], integrated run-time systems such as CPU MISER [22], CoScale [23], or energy quota-based systems like Intel's RAPL [24].…”

Section: Dynamic Voltage and Frequency Scalingmentioning

confidence: 99%

Low-Overhead Reinforcement Learning-Based Power Management Using 2QoSM

Giardino

Schwyn

Ferri

et al. 2022

JLPEA

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With the computational systems of even embedded devices becoming ever more powerful, there is a need for more effective and pro-active methods of dynamic power management. The work presented in this paper demonstrates the effectiveness of a reinforcement-learning based dynamic power manager placed in a software framework. This combination of Q-learning for determining policy and the software abstractions provide many of the benefits of co-design, namely, good performance, responsiveness and application guidance, with the flexibility of easily changing policies or platforms. The Q-learning based Quality of Service Manager (2QoSM) is implemented on an autonomous robot built on a complex, powerful embedded single-board computer (SBC) and a high-resolution path-planning algorithm. We find that the 2QoSM reduces power consumption up to 42% compared to the Linux on-demand governor and 10.2% over a state-of-the-art situation aware governor. Moreover, the performance as measured by path error is improved by up to 6.1%, all while saving power.

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Section: Dynamic Voltage and Frequency Scalingmentioning

confidence: 99%

Low-Overhead Reinforcement Learning-Based Power Management Using 2QoSM

Giardino

Schwyn

Ferri

et al. 2022

JLPEA

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Research on quasi-dynamic calibration model of plastic sensitive element based on neural networks

Wang

Kong

Yang³

et al. 2017

J. Phys.: Conf. Ser.

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Abstract. Quasi-dynamic calibration accuracy of the plastic sensitive element depends on the accuracy of the fitting model between pressure and deformation. By using the excellent nonlinear mapping ability of RBF (Radial Basis Function) neural network, a calibration model is established which use the peak pressure as the input and use the deformation of the plastic sensitive element as the output in this paper. The calibration experiments of a batch of copper cylinders are carried out on the quasi-dynamic pressure calibration device, which pressure range is within the range of 200MPa to 700MPa. The experiment data are acquired according to the standard pressure monitoring system. The network train and study are done to quasi dynamic calibration model based on neural network by using MATLAB neural network toolbox. Taking the testing samples as the research object, the prediction accuracy of neural network model is compared with the exponential fitting model and the second-order polynomial fitting model. The results show that prediction of the neural network model is most close to the testing samples, and the accuracy of prediction model based on neural network is better than 0.5%, respectively one order higher than the second-order polynomial fitting model and two orders higher than the exponential fitting model. The quasi-dynamic calibration model between pressure peak and deformation of plastic sensitive element, which is based on neural network, provides important basis for creating higher accuracy quasi-dynamic calibration table.

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