Automated Synthesis of Commutative Approximate Arithmetic Operators
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F24%3APU152033" target="_blank" >RIV/00216305:26230/24:PU152033 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/CEC60901.2024.10612202" target="_blank" >http://dx.doi.org/10.1109/CEC60901.2024.10612202</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CEC60901.2024.10612202" target="_blank" >10.1109/CEC60901.2024.10612202</a>
Alternative languages
Result language
angličtina
Original language name
Automated Synthesis of Commutative Approximate Arithmetic Operators
Original language description
Approximate computing, leveraging the inherent resilience to errors, emerges as a promising strategy for reducing power consumption in digital systems. The primary objective of this paper is to introduce an efficient method based on Cartesian Genetic Programming for designing approximate arithmetic circuits with commutative property. Specifically, this work focuses on the design of 8-bit approximate multipliers and 32-bit approximate adders, both serving as foundational components for hardware accelerators in neural networks. We have identified that while the design of commutative approximate adders poses no issues for evolution, the design of commutative approximate multipliers represents a challenging problem causing the commonly used CGP stuck at highly sub-optimal solutions. In response to this challenge, we propose a novel application-specific mutation operator. This operator significantly enhances the efficiency of the search process, enabling the discovery of solutions that were previously unreachable. The achieved results revealed that imposing the requirement for a commutative property does not substantially compromise the quality-error trade-offs of the obtained approximate circuits, making the resulting Pareto front comparable to that of unconstrained designs.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA22-02067S" target="_blank" >GA22-02067S: AppNeCo: Approximate Neurocomputing</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings
ISBN
979-8-3503-0836-5
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
1-8
Publisher name
IEEE Computer Society
Place of publication
Yokohama
Event location
Yokohama
Event date
Jun 30, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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