Partition and Propagate

Leveraging the inherent error tolerance of a vast number of application domains that are rapidly growing, approximate computing arises as a design alternative to improve the efficiency of our computing systems by trading accuracy for energy savings. However, the requirement for computational accuracy is not fixed. Controlling the applied level of approximation dynamically at runtime is a key to effectively optimize energy, while still containing and bounding the induced errors at runtime. In this paper, we propose and implement an automatic and circuit independent design framework that generates approximate circuits with dynamically reconfigurable accuracy at runtime. The generated circuits feature varying accuracy levels, supporting also accurate execution. Extensive experimental evaluation, using industry strength flow and circuits, demonstrates that our generated approximate circuits improve the energy by up to 41% for 2% error bound and by 17.5% on average under a pessimistic scenario that assumes full accuracy requirement in the 33% of the runtime. To demonstrate further the efficiency of our framework, we considered two state-of-the-art technology libraries which are a 7nm conventional FinFET and an emerging technology that boosts performance at a high cost of increased dynamic power. INDEX TERMS Approximate computing, approximate design automation, dynamically reconfigurable accuracy, low power.

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Partition and Propagate

Cited by 22 publications

References 16 publications

Efficient Use of Randomisation Algorithms for Probability Prediction in Baccarat Using: Monte Carlo and Las Vegas Method

Efficient Use of Randomisation Algorithms for Probability Prediction in Baccarat Using: Monte Carlo and Las Vegas Method

Linear-time error calculation for approximate adders

Design Automation of Approximate Circuits With Runtime Reconfigurable Accuracy

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