scholarly journals Mixing Global and Local Competition in Genetic Optimization based Design Space Exploration of Analog Circuits

2005 ◽  
Author(s):  
A. Somani ◽  
P.P. Chakrabarti ◽  
A. Patra
Keyword(s):  
Analog Circuits ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Genetic Optimization ◽  
Local Competition ◽  
Global And Local ◽  
Optimization Based Design

Multi-objective optimization and analysis for the design space exploration of analog circuits and solar cells

2017 ◽  
Vol 62 ◽  
pp. 373-383 ◽  
Author(s):  
Andrea Patanè ◽  
Andrea Santoro ◽  
Piero Conca ◽  
Giovanni Carapezza ◽  
Antonino La Magna ◽  
...  
Keyword(s):  
Solar Cells ◽  
Analog Circuits ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Multi Objective Optimization ◽  
Multi Objective

P3GA: An Algorithm for Technology Characterization

2015 ◽  
Vol 137 (1) ◽  
Author(s):  
Edgar Galvan ◽  
Richard J. Malak
Keyword(s):  
Design Automation ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Numerical Test ◽  
Pareto Frontier ◽  
Test Problems ◽  
Genetic Optimization ◽  
Technology Evaluation ◽  
Mathematical Basis

It is important for engineers to understand the capabilities and limitations of the technologies they consider for use in their systems. However, communicating this information can be a challenge. Mathematical characterizations of technical capabilities are of interest as a means to reduce ambiguity in communication and to increase opportunities to utilize design automation methods. The parameterized Pareto frontier (PPF) was introduced in prior work as a mathematical basis for modeling technical capabilities. One advantage of PPFs is that, in many cases, engineers can model a system by composing frontiers of its components. This allows for rapid technology evaluation and design space exploration. However, finding the PPF can be difficult. The contribution of this article is a new algorithm for approximating the PPF, called predictive parameterized Pareto genetic algorithm (P3GA). The proposed algorithm uses concepts and methods from multi-objective genetic optimization and machine learning to generate a discrete approximation of the PPF. If needed, designers can generate a continuous approximation of the frontier by generalizing beyond these data. The algorithm is explained, its performance is analyzed on numerical test problems, and its use is demonstrated on an engineering example. The results of the investigation indicate that P3GA may be effective in practice.

Design Space Exploration of a 512KB STT-Assisted SOT MRAM Cache

2020 ◽  
Author(s):  
Adrian G. Caburnay ◽  
Jonathan Gabriel S.A. Reyes ◽  
Anastacia P. Ballesil-Alvarez ◽  
Maria Theresa G. de Leon ◽  
John Richard E. Hizon ◽  
...  
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration

scholarly journals Optode Design Space Exploration for Clinically-robust Non-invasive Fetal Oximetry

2019 ◽  
Vol 18 (5s) ◽  
pp. 1-22 ◽  
Author(s):  
Daniel D. Fong ◽  
Vivek J. Srinivasan ◽  
Kourosh Vali ◽  
Soheil Ghiasi
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Non Invasive
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