Asymmetrical and lower bounded support vector regression for power estimation

Abstract

In an energy aware environment, designers frequently turn to advanced power reduction techniques such as power shutoff and multi-supply-voltage architectures. In order to implement these techniques, it is important that power estimates be made. Power prediction is a critical necessity as chip sizes continually decrease and the desire for low power consumption is a foremost design objective. For such predictions, it is crucial to avoid underestimating power since reliability issues and possible chip damage might occur. It becomes necessary to eliminate or strictly limit underestimations by relaxing accuracy constraints while decreasing the likelihood that the estimation undershoots the actual value. Our novel approach, Asymmetrical and Lower Bounded Support Vector Regression modifies the Support Vector Regression technique by Vapnik and provides accurate prediction while maintaining a low number of underestimates. We tested our approach on two different power data sets and achieved accuracy rates of 5.72% and 5.06% relative percentage error while keeping the number of underestimates below 2.81% and 1.74%.

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

“…The ANN model might reflect the PC and Chl-a features at low concentrations, with multiple nodes and layers, better than the SVR model. Moreover, [69] discussed the local underestimation of SVR, in which the kernel location was supposed to be the center of the epsilon-tube, but the SVR only allowed a small number of estimated values to fall below the observed values.…”

Support Vector Regression

“…therefore, this approach performs the inference and decision stages in one step. Justification: In many instances of approximation, there is an uneven consequence of misprediction, based on whether the error is above or below the target value (Stockman et al 2012a(Stockman et al , 2012b. For example, in power prediction an incorrect low estimate may be of much more concern than an overestimate.…”

“…Approach: Asymmetrical and lower-bounded SVR (ALB-SVR) was proposed by Stockman, Awad, and Khanna (2012a). This approach modifies the SVR loss functions and corresponding error functions, such that the e-tube is only above the function, as demonstrated in Figure 4-6.…”