ApproxIt: An approximate computing framework 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. Many of these applications employ iterative methods for solution-finding, wherein a sequence of improving approximate solutions are generated before reaching the final converged solution. In this work, we propose ApproxIt, a novel approximate computing framework for iterative methods with quality guarantees. To be specific, we present a lightweight quality estimator that is able to capture the solution quality of each iteration and use it to guide the selection of approximate computing mode in the next iteration. 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.