Dynamic frequency scaling (DFS) is one of the most important approaches for on-the-fly power optimization in modern-day processors. Owing to the trend of chip size shrinkage and increasing the complexity of system design, the problem of achieving an efficient DFS depends upon multi-parametric, non-linear optimization. Hence, it becomes extremely important to identify an optimal underclocking frequency on-the-fly, which depends upon numerous parameters that do not share direct relationship amongst each other. This paper proposes a machine learning approach to DFS of a ubiquitous single-core processor. Several performance parameters of the processor were monitored under an application of a number of clocking frequencies. The dataset thus generated was used to train four artificial neural networks (ANNs) viz. generalized regression (GRNN), decision tree classifier, random forest classifier and backpropagation technique. Under changing parametric conditions of the proposed network, the modes were fit to data while running three applications, i.e. 64- and 1024-point fast fourier transform (FFT) and basicmath applications. The performance of all ANNs was found to be promising and good generalization was obtained with all datasets. In the view of optimizing both speed and power of a system, the results indicate towards suitability of trained GRNN for on-chip deployment for implementing DFS.
<p> Real Time embedded systems are highly complex due to interactions and interdependencies between various hardware/software units and policies of the processors with applications running on it. To deal with fluctuating workloads and subsequent tasks, smart adaptability of supply clock and voltage is required in order to optimize power without compromising on the performance. This is done using Dynamic Voltage and Frequency Scaling (DVFS) technique. An improved version of DVFS is proposed in this paper which treats it as a recurrent problem with an aim to capture the intricate dependencies amongst various factors influencing the operation. The authors have employed application independent- Radial Basis Neural Network to generate series of predicted frequencies for current workload of the processor, followed by seq2seq-LSTM based encoder decoder model using Attention to decide if the frequency generated by the Artificial Neural Network (ANN) model is optimum from power conservation point of view. The proposed model predicts the workload and then compares the predicted frequency to the critical value or deadline of the current task. The experiments were conducted on a single core processor on which a benchmark application was run, and promising prediction accuracy rates were obtained without incurring degradation of critical performance parameters. </p>
<p> Real Time embedded systems are highly complex due to interactions and interdependencies between various hardware/software units and policies of the processors with applications running on it. To deal with fluctuating workloads and subsequent tasks, smart adaptability of supply clock and voltage is required in order to optimize power without compromising on the performance. This is done using Dynamic Voltage and Frequency Scaling (DVFS) technique. An improved version of DVFS is proposed in this paper which treats it as a recurrent problem with an aim to capture the intricate dependencies amongst various factors influencing the operation. The authors have employed application independent- Radial Basis Neural Network to generate series of predicted frequencies for current workload of the processor, followed by seq2seq-LSTM based encoder decoder model using Attention to decide if the frequency generated by the Artificial Neural Network (ANN) model is optimum from power conservation point of view. The proposed model predicts the workload and then compares the predicted frequency to the critical value or deadline of the current task. The experiments were conducted on a single core processor on which a benchmark application was run, and promising prediction accuracy rates were obtained without incurring degradation of critical performance parameters. </p>
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