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Abstract-This paper presents a highly energy efficient alternative algorithm to the conventional workload averaging technique for voltage quantized dynamic voltage scaling. This algorithm incorporates the strengths of the conventional workload averaging technique and our previously proposed Rate Selection Algorithm, resulting in higher energy savings while minimizing the buffer size requirement and improving the overall system stability by minimizing the number of voltage transitions. Our experimental work using the Forward Mapped Inverse Discrete Cosine Transform computation (FMIDCT) as the variable workload computation, nine 300-frame MPEG-2 video sequences as the test data, and a 4-level voltage quantization shows that our algorithm produces better energy savings in all test cases when compared to the workload averaging technique, and the maximum energy saving for the test cases was 23%.

E-mail prioritization involves placing all of the 'useful' or 'good' unread e-mails at the top of the inbox, and all of the bad ones at the bottom. We use two cognitive decision models-a rational model, which considers all of the available information, and a fast and frugal model that uses one reason decision makingto prioritize e-mails. Experimental results, using real data obtained by unobtrusively logging e-mail user behavior, show that the fast and frugal model is just as effective as the rational model. The results also show that a Bayesian approach to learning is superior to the standard frequentist approach, because it balances the competing demands of exploration and exploitation in finding good e-mails. We use the results to draw some applied conclusions about the development of an email prioritization system, and note some theoretical implications of the results for the cognitive modeling of human decision making in general.

Abstract-This paper presents a highly energy efficient alternative algorithm to the conventional workload averaging technique for voltage quantized dynamic voltage scaling. This algorithm incorporates the strengths of the conventional workload averaging technique and our previously proposed Rate Selection Algorithm, resulting in higher energy savings while minimizing the buffer size requirement and improving the overall system stability by minimizing the number of voltage transitions. Our experimental work using the Forward Mapped Inverse Discrete Cosine Transform computation (FMIDCT) as the variable workload computation, nine 300-frame MPEG-2 video sequences as the test data, and a 4-level voltage quantization shows that our algorithm produces better energy savings in all test cases when compared to the workload averaging technique, and the maximum energy saving for the test cases was 23%.