Runtime system-level power estimation is essential for dynamic power adaptation in integrated circuits. Indirect power estimation using a CPU performance monitoring unit (PMU) is widely used in modern microprocessors for its low cost. However, the existing CPU PMUs only monitor the activities of core and cache, resulting in accuracy limitation in power estimation for systems containing heterogeneous devices. In this paper, an onchip bus PMU (OCB PMU) is proposed for PKU-DSPII SOC to achieve accurate system-level power estimation by taking range of peripheral devices into account. By monitoring the on-chip bus signals which indicate almost all the information of the system behavior, an OCB PMU is able to recognize different types of devices. By imitating the internal behavior of every device triggered by OCB signals using an energy state machine (ESM), an OCB PMU is capable of accurate power estimation of heterogeneous devices with complicated behavior. The proposed PMUs can also flexibly adapt to different SoC architectures.
A robust estimation method, Balanced Least Absolute Value Estimator (BLAVE), is introduced and compared with the traditional RANdom SAmple Consensus (RANSAC) method. The comparison is performed empirically by applying both estimators on the camera motion parameters estimation problem. A linearised model for this estimation problem is derived.The tests were performed on a simulated scene with added random noise and gross errors as well as on actual images taken by a mobile mapping system. The greatest advantage of BLAVE is that it processes all observations at once as well as its median-like property: the estimated parameters are not influenced by the size of the outliers. It can tolerate up to 50% outliers in data and still produce accurate results. The greatest disadvantage of RANSAC is that the results are not repeatable because of the random sampling of data. Moreover, the results are less accurate, because RANSAC generally does not produce a 'best-fit' parameter estimation. The number of trials, which must be tested by RANSAC to find a reasonable solution, depends on the portion of outliers in data. The computational time for BLAVE does not depend on the portion of outliers in the observations, but it grows with the number of observations, same as RANSAC.
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