SagTree: Towards Efficient Mutation in Evolutionary Circuit Approximation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU142950" target="_blank" >RIV/00216305:26230/22:PU142950 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2210650221001486" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2210650221001486</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.swevo.2021.100986" target="_blank" >10.1016/j.swevo.2021.100986</a>

Jazyk výsledku

angličtina

Název v původním jazyce

SagTree: Towards Efficient Mutation in Evolutionary Circuit Approximation

Popis výsledku v původním jazyce

Approximate circuits that trade the chip area for the quality of results play a key role in the development of energy-aware systems. Designing complex approximate circuits is, however, a very difficult and computationally demanding process. Evolutionary approximation - in particular, the method of Cartesian Genetic Programming (CGP) - currently represents one of the most successful approaches for automated circuit approximation. In this paper, we thoroughly investigate mutation operators for CGP with respect to the performance of circuit approximation. We design a novel dedicated operator that combines the classical single active gene mutation with a node deactivation operation (eliminating a part of the circuit forming a tree from an active gate). We show that our new operator significantly outperforms other operators on a wide class of approximation problems (such as 16 bit multipliers and dividers) and thus improves the performance of the state-of-the-art approximation techniques. Our results are grounded on a rigorous statistical evaluation including 39 approximation scenarios and 14,000 runs.

Název v anglickém jazyce

SagTree: Towards Efficient Mutation in Evolutionary Circuit Approximation

Popis výsledku anglicky

Approximate circuits that trade the chip area for the quality of results play a key role in the development of energy-aware systems. Designing complex approximate circuits is, however, a very difficult and computationally demanding process. Evolutionary approximation - in particular, the method of Cartesian Genetic Programming (CGP) - currently represents one of the most successful approaches for automated circuit approximation. In this paper, we thoroughly investigate mutation operators for CGP with respect to the performance of circuit approximation. We design a novel dedicated operator that combines the classical single active gene mutation with a node deactivation operation (eliminating a part of the circuit forming a tree from an active gate). We show that our new operator significantly outperforms other operators on a wide class of approximation problems (such as 16 bit multipliers and dividers) and thus improves the performance of the state-of-the-art approximation techniques. Our results are grounded on a rigorous statistical evaluation including 39 approximation scenarios and 14,000 runs.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GJ20-02328Y" target="_blank" >GJ20-02328Y: CAQtuS: Počítačem podporovaná kvantitativní syntéza</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Swarm and Evolutionary Computation

ISSN

2210-6502

e-ISSN

2210-6510

Svazek periodika

69

Číslo periodika v rámci svazku

100986

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

10

Strana od-do

1-10

Kód UT WoS článku

000820715300004

EID výsledku v databázi Scopus

2-s2.0-85117233906