On-Chip Intelligent Dynamic Frequency Scaling for Real-Time Systems
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To curb redundant power consumption in portable embedded and real-time applications, processors are equipped with various Dynamic Voltage and Frequency Scaling (DVFS) techniques. The accuracy of the prediction of the operating frequency of any such technique determines how power-efficient it makes a processor for a variety of programs and users. But, in the recent techniques, the focus has been too much on saving power, thus, ignoring the user-satisfaction metric, i.e. performance. The DVFS technique used to save power, in turn, introduces unwanted latency due to the high complexity of the algorithm. Also, many of the modern DVFS techniques provide feedback manually triggered by the user to change the frequency to conserve energy efficiently, thus, further increasing the reaction time. In this paper, we imple- ment a novel Artificial Neural Networks-driven frequency scaling methodology, which makes it possible to save power and boost performance at the same time, implicitly i.e. without any feedback from the user. Also, to make the system more inclusive concerning the kinds of processes run on it, we trained the ANN not only for CPU-intensive programs but also on the ones that are more memory-bound, i.e. which have frequent memory accesses during its average CPU cycle. The proposed technique has been evaluated on Intel i7-4720HQ Haswell processor and has shown performance boost by up to 20%, SoC power savings up to 16%, and Performance per Watt improvement by up to 30%, as compared to the existing DVFS technique. An open-source memory-intensive benchmark kit called Mibench was used to verify the utility of the suggested technique.