A Framework for Heterotic Computing

Computational devices combining two or more different parts, one controlling the operation of the other, for example, derive their power from the interaction, in addition to the capabilities of the parts. Non-classical computation has tended to consider only single computational models: neural, anal...

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Main Authors: Susan Stepney, Viv Kendon, Peter Hines, Angelika Sebald
Format: Article
Language:English
Published: Open Publishing Association 2012-10-01
Series:Electronic Proceedings in Theoretical Computer Science
Online Access:http://arxiv.org/pdf/1210.0621v1
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spelling doaj-5c2be5703416432e97aa3a5717d3986d2020-11-25T02:44:01ZengOpen Publishing AssociationElectronic Proceedings in Theoretical Computer Science2075-21802012-10-0195Proc. QPL 201126327310.4204/EPTCS.95.18A Framework for Heterotic ComputingSusan StepneyViv KendonPeter HinesAngelika SebaldComputational devices combining two or more different parts, one controlling the operation of the other, for example, derive their power from the interaction, in addition to the capabilities of the parts. Non-classical computation has tended to consider only single computational models: neural, analog, quantum, chemical, biological, neglecting to account for the contribution from the experimental controls. In this position paper, we propose a framework suitable for analysing combined computational models, from abstract theory to practical programming tools. Focusing on the simplest example of one system controlled by another through a sequence of operations in which only one system is active at a time, the output from one system becomes the input to the other for the next step, and vice versa. We outline the categorical machinery required for handling diverse computational systems in such combinations, with their interactions explicitly accounted for. Drawing on prior work in refinement and retrenchment, we suggest an appropriate framework for developing programming tools from the categorical framework. We place this work in the context of two contrasting concepts of "efficiency": theoretical comparisons to determine the relative computational power do not always reflect the practical comparison of real resources for a finite-sized computational task, especially when the inputs include (approximations of) real numbers. Finally we outline the limitations of our simple model, and identify some of the extensions that will be required to treat more complex interacting computational systems.http://arxiv.org/pdf/1210.0621v1
collection DOAJ
language English
format Article
sources DOAJ
author Susan Stepney
Viv Kendon
Peter Hines
Angelika Sebald
spellingShingle Susan Stepney
Viv Kendon
Peter Hines
Angelika Sebald
A Framework for Heterotic Computing
Electronic Proceedings in Theoretical Computer Science
author_facet Susan Stepney
Viv Kendon
Peter Hines
Angelika Sebald
author_sort Susan Stepney
title A Framework for Heterotic Computing
title_short A Framework for Heterotic Computing
title_full A Framework for Heterotic Computing
title_fullStr A Framework for Heterotic Computing
title_full_unstemmed A Framework for Heterotic Computing
title_sort framework for heterotic computing
publisher Open Publishing Association
series Electronic Proceedings in Theoretical Computer Science
issn 2075-2180
publishDate 2012-10-01
description Computational devices combining two or more different parts, one controlling the operation of the other, for example, derive their power from the interaction, in addition to the capabilities of the parts. Non-classical computation has tended to consider only single computational models: neural, analog, quantum, chemical, biological, neglecting to account for the contribution from the experimental controls. In this position paper, we propose a framework suitable for analysing combined computational models, from abstract theory to practical programming tools. Focusing on the simplest example of one system controlled by another through a sequence of operations in which only one system is active at a time, the output from one system becomes the input to the other for the next step, and vice versa. We outline the categorical machinery required for handling diverse computational systems in such combinations, with their interactions explicitly accounted for. Drawing on prior work in refinement and retrenchment, we suggest an appropriate framework for developing programming tools from the categorical framework. We place this work in the context of two contrasting concepts of "efficiency": theoretical comparisons to determine the relative computational power do not always reflect the practical comparison of real resources for a finite-sized computational task, especially when the inputs include (approximations of) real numbers. Finally we outline the limitations of our simple model, and identify some of the extensions that will be required to treat more complex interacting computational systems.
url http://arxiv.org/pdf/1210.0621v1
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