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In this work, we present the ApproxFPGAs methodology that uses machine learning models to reduce the exploration time for analyzing the state-of-the-art ASIC-based ACs to determine the set of pareto-optimal FPGA-based ACs. Unlike our work, in all of the above the risk inherent in exchange-rate fluctuations and their affect on ASIC pricing is not addressed. This effect is more prominent for low values of the injected charge while for values above 10 fC, it becomes less pronounced due to the saturation of the preamplifier. They feature upgrades like waterproof leather, gortex linings and Vibram soles while still pulling off the sneaker look. The complete procedure can be divided into two sub-parts, (i) the first part deals with the training and testing of the ML models, which can be used to efficiently estimate the hardware resources of a given approximate arithmetic design, while (ii) the second part deals with the construction of the pareto-optimal FPGA-ACs, which can be deployed in error-tolerant applications. Note, by pareto-optimal approximate circuits we mean the set of all circuits that are not dominated by any other circuit from the set of circuits in the library in terms of the evaluation metrics. Note, the use of other state-of-the-art designs is orthogonal to our approach and they can be appropriately included, with necessary modifications, in the library of approximate circuits

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