Design-space exploration for low-power manycore design is a daunting and time-consuming task which requires some complex tools and frameworks to achieve. In the presence of process variation, the problem becomes even more challenging, especially the time associated with trial-and-error selection of the proper options in the tools to obtain the optimal power dissipation. The key contribution of this work is the novel use of machine learning to speed up the design process by embedding the tool expertise needed for low power design-space exploration for manycores into a trained neural network. To enable this, we first generate a large volume of data for 36000 benchmark applications by running them under all possible configurations to find the optimal one in terms of power. This is done using our own tool called LVSiM, a holistic manycore optimization program including process variations. A neural network is trained with this information to build in the expertise. A second contribution of this work is to define a new set of features, relevant to power and performance optimization, when training the neural network. At design time, the trained neural network is used to select the proper options on behalf of the user based on the features of any new application. However, one problem encountered with this approach is that the database constructed for machine learning has many outliers due to randomness associated with process variation which creates a major headache for classification - the supervised learning task performed by neural networks. The third key contribution of this work is a novel data coercion algorithm used as a corrective measure to handle the outliers. The proposed data coercion scheme produces results that are within 3.9% of the optimal power consumption compared to 7% without data coercion. Furthermore, the proposed method is about an order of magnitude faster than a heuristic approach and two orders of magnitude faster than a brute-force approach for design-space exploration.@en

In this paper, we study the impact of the idle/dynamic power consumption ratio on the effectiveness of a multi-V _dd /frequency manycore design. We propose a new tool called LVSiM (a Low-Power and Variation-Aware Manycore Simulator) to carry out the experiments. It is a novel manycore simulator targeted towards low-power optimization methods including within-die process and workload variations. LVSiM provides a holistic platform for multi-V _dd /frequency voltage island analysis, optimization, and design. It provides a tool for the early design exploration stage to analyze large-scale manycores with a given number of cores on 3D-stacked layers, network-on-chip communication busses, technology parameters, voltage and frequency values, and power grid parameters, using a variety of different optimization methods. LVSiM has been calibrated with Sniper/McPAT at a nominal frequency, and then the energy-delay-product (EDP) numbers were compared after frequency scaling. The average error is shown to be 10% after frequency scaling, which is sufficient for our purposes. The experiments in this work are carried out for different Idle/Dynamic ratios considering 1260 benchmarks with task sizes ranging from 4000 to 16 000 executing on 3200 cores. The best configurations are shown to produce on average 20.7% to 24.6% EDP savings compared to the nominal configuration. Traditional scheduling methods are used in the nominal configuration with the unused cores switched off. In addition, we show that, as the Idle/Dynamic ratio increases, the multi-V _dd /frequency approach becomes less effective. In the case of a high Idle/Dynamic ratio, the minimum EDP can be achieved through switching off unused cores as opposed to using a multi-V _dd /frequency approach. This conclusion is important, especially in the dark-silicon era, where switching cores on and/or off as needed is a common practice.

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