# Comparing and Combining Approximate Computing Frameworks

arXiv: https://arxiv.org/abs/2102.08771 · HTML: https://mlsanity.com/paper/2102.08771/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 60 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 2 of 2)

- [ALERT: Accurate Learning for Energy and Timeliness](https://mlsanity.com/paper/1911.00119/) (2019)
- [Orthogonalized SGD and Nested Architectures for Anytime Neural Networks](https://mlsanity.com/paper/2008.06635/) (2020)

## Abstract (arXiv)

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.