ApproxIt: A Quality Management Framework of Approximate Computing for Iterative Methods

Approximate computing, being able to tradeoff computation quality (e.g., accuracy) and computational effort (e.g., energy) for error-tolerant applications such as media processing and the emerging recognition, mining, and synthesis (RMS) applications, has gained significant traction in recent years. With approximate computing, we expect to obtain acceptable results, but how do we make sure the quality of the final results are good enough? This challenging problems remains largely unexplored. As many of the RMS applications employ iterative methods (IMs) for solution-finding, wherein a sequence of improving approximate solutions are generated before reaching the final converged solution, in this paper, we propose ApproxIt, a novel quality management framework of approximate computing dedicated for IMs with quality guarantees. ApproxIt is comprised of two stages: 1) offline stage and 2) online stage. To be specific, at offline stage, we first analyze the manifold of parameter space to identify the given problem as convex case or nonconvex case at the offline stage. And for each case, we propose the corresponding runtime dynamic quality calibration scheme and reconfiguration control policy. Then during runtime, our proposed lightweight quality estimator will evaluate the intermediate quality at specific calibration iteration, which is determined by the novel Markov model-based calibration scheme. If quality violation occurs, the configuration control policy will select the most appropriate approximate computing mode for the following iterations. With the proposed dynamic effort scaling technique, ApproxIt is able to dramatically improve application energy efficiency under quality guarantees, as demonstrated in our experimental results.