MIPAC: Dynamic Input-Aware Accuracy Control for Dynamic Auto-Tuning of Iterative Approximate Computing

For many applications that exhibit strong error resilience, such as machine learning and signal processing, energy efficiency and performance can be dramatically improved by allowing for slight errors in intermediate computations. Iterative methods (IMs), wherein the solution is improved over multiple executions of an approximation algorithm, allow for energy-quality trade-off at run-time by adjusting the number of iterations (NOI). However, in prior IM circuits, NOI adjustment has been made based on a pre-characterized NOI-quality mapping, which is input-agnostic thus results in an undesirable large variation in output quality. In this paper, we propose a novel design framework that incorporates a lightweight quality controller that makes input-dependent predictions on the output quality and determines the optimal NOI at run-time. The proposed quality controller is composed of accurate yet low-overhead NOI predictors, generated by a novel logic reduction technique. We evaluate the proposed design framework on several IM circuits and demonstrate significant improvements in energy-quality performance.