Using Approximate Computing for the Calculation of Inverse Matrix p-th Roots

TL;DR

This paper explores the use of approximate computing techniques to efficiently calculate inverse matrix p-th roots, demonstrating potential for reduced computational effort and power consumption in scientific computing applications.

Contribution

It introduces the application of approximate computing to iterative algorithms for inverse matrix p-th roots, highlighting its benefits in scientific computing contexts.

Findings

Significant reduction in computational effort.

Potential for lower power consumption.

Effective use on specialized hardware.

Abstract

Approximate computing has shown to provide new ways to improve performance and power consumption of error-resilient applications. While many of these applications can be found in image processing, data classification or machine learning, we demonstrate its suitability to a problem from scientific computing. Utilizing the self-correcting behavior of iterative algorithms, we show that approximate computing can be applied to the calculation of inverse matrix p-th roots which are required in many applications in scientific computing. Results show great opportunities to reduce the computational effort and bandwidth required for the execution of the discussed algorithm, especially when targeting special accelerator hardware.