Prediction-Based Quality Control for Approximate Accelerators

Approximate accelerators are an emerging type of accelerator that trade output quality for significant gains in performance and energy efficiency. Conventionally, the approximate accelerator is always invoked in lieu of a frequently executed region of code (e.g., a function in a loop). However, always invoking the accelerator results in a fixed degree of error that may not be desirable. Our core idea is to predict whether each individual accelerator invocation will lead to an undesirable quality loss in the final output. We therefore design and evaluate predictors that only leverage information local to that specific potential invocation. If the predictor speculates that a large quality degradation is likely, it directs the core to run the original precise code instead. We use neural networks as an alternative prediction mechanism for quality control that also provides a realistic reference point to evaluate the effectiveness of our table-based predictor. Our evaluation comprises a set of benchmarks with diverse error behavior. For these benchmarks a table-based predictor with eight tables each of size 0.5KB achieves 2.6× average speedup and 2.8× average energy reduction with a 5% error requirement. The neural predictor yields 4% and 17% larger performance and energy gains, respectively. On average, an idealized oracle predictor with prior knowledge about all invocations achieves only 26% more performance and 37% more energy benefits compared to the table-based predictor.