On the Codesign of Scientific Experiments and Industrial Systems

TL;DR

This paper explores the co-design of hardware and software in complex scientific and industrial systems, emphasizing the importance of integrated optimization to achieve maximum performance.

Contribution

It discusses the emergence of hardware-software coupling and proposes co-design procedures to optimize the global utility functions of such systems.

Findings

Coupling between hardware and software can hinder optimization if ignored.

Co-design procedures can identify global maxima in system utility functions.

Opportunities exist to improve methods bridging science and industry.

Abstract

The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for industrial or societal applications the common issue of addressing the inter-relation between parameters describing the hardware used in data production and parameters used to analyse those data. While in many cases this coupling can be ignored -- when the problem can be successfully factored into simpler sub-tasks and the latter addressed serially -- there are situations in which that approach fails to converge to the absolute maximum of expected performance, as it results in a mis-alignment of the optimized hardware and software solutions. In this work we consider a few use cases of interest in fundamental science collected primarily from particle physics and related areas, and a pot-pourri of industrial and societal applications where the matter is similarly of relevance. We discuss the emergence of strong hardware-software coupling in some of those systems, as well as co-design procedures that may be deployed to identify the global maximum of their relevant utility functions. We observe how numerous opportunities exist to advance methods and tools for hardware-software co-design optimization, bridging fundamental science and industry through application- and challenge-driven projects, and shaping the future of scientific experiments and industrial systems.