Comparing and Combining Approximate Computing Frameworks

TL;DR

This paper introduces VIPER and BOA, tools for comparing and combining approximation frameworks to efficiently explore trade-offs between accuracy and performance in approximate computing.

Contribution

It presents VIPER for visual comparison of trade-off spaces and BOA for rapid exploration of Pareto-efficient points across combined frameworks.

Findings

VIPER provides quicker, more convenient trade-off comparisons.

BOA explores 14x fewer configurations and finds 35% more Pareto points.

The methods enable effective combination of different approximation techniques.

Abstract

Approximate computing frameworks configure applications so they can operate at a range of points in an accuracy-performance trade-off space. Prior work has introduced many frameworks to create approximate programs. As approximation frameworks proliferate, it is natural to ask how they can be compared and combined to create even larger, richer trade-off spaces. We address these questions by presenting VIPER and BOA. VIPER compares trade-off spaces induced by different approximation frameworks by visualizing performance improvements across the full range of possible accuracies. BOA is a family of exploration techniques that quickly locate Pareto-efficient points in the immense trade-off space produced by the combination of two or more approximation frameworks. We use VIPER and BOA to compare and combine three different approximation frameworks from across the system stack, including: one that changes numerical precision, one that skips loop iterations, and one that manipulates existing application parameters. Compared to simply looking at Pareto-optimal curves, we find VIPER's visualizations provide a quicker and more convenient way to determine the best approximation technique for any accuracy loss. Compared to a state-of-the-art evolutionary algorithm, we find that BOA explores 14x fewer configurations yet locates 35% more Pareto-efficient points.