scholarly journals Design automation and design space exploration for quantum computers

2017 ◽  
Author(s):  
Mathias Soeken ◽  
Martin Roetteler ◽  
Nathan Wiebe ◽  
Giovanni De Micheli
Keyword(s):  
Design Automation ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Quantum Computers

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.

High-Level Design Space Exploration of Embedded Systems Using the Model-Driven Engineering and Aspect-Oriented Design Approaches

2010 ◽  
pp. 114-146
Author(s):  
Marcio Ferreira da Silva Oliveira ◽  
Marco Aurelio Wehrmeister ◽  
Francisco Assis do Nascimento ◽  
Carlos Eduardo Pereira
Keyword(s):  
Embedded Systems ◽  
Design Automation ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
System Quality ◽  
Model Driven Engineering ◽  
Model Driven ◽  
Level Design ◽  
High Level

Modern embedded systems have increased their functionality by using a large amount and diversity of hardware and software components. Realizing the expected system functionality is a complex task. Such complexity must be managed in order to decrease time-to-market and increase system quality. This chapter presents a method for high-level design space exploration (DSE) of embedded systems that uses model-driven engineering (MDE) and aspect-oriented design (AOD) approaches. The modelling style and the abstraction level open new design automation and optimization opportunities, thus improving the overall results. Furthermore, the proposed method achieves better reusability, complexity management, and design automation by exploiting both MDE and AOD approaches. Preliminary results regarding the use of the proposed method are presented.

scholarly journals Design Space Exploration of Hybrid Quantum–Classical Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2980
Author(s):  
Muhammad Kashif ◽  
Saif Al-Kuwari
Keyword(s):  
Neural Networks ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Quantum Computers ◽  
Computational Advantage ◽  
Systematic Methodology ◽  
Universal Quantum ◽  
Speed Up ◽  
Hybrid Neural Networks

The unprecedented success of classical neural networks and the recent advances in quantum computing have motivated the research community to explore the interplay between these two technologies, leading to the so-called quantum neural networks. In fact, universal quantum computers are anticipated to both speed up and improve the accuracy of neural networks. However, whether such quantum neural networks will result in a clear advantage on noisy intermediate-scale quantum (NISQ) devices is still not clear. In this paper, we propose a systematic methodology for designing quantum layer(s) in hybrid quantum–classical neural network (HQCNN) architectures. Following our proposed methodology, we develop different variants of hybrid neural networks and compare them with pure classical architectures of equivalent size. Finally, we empirically evaluate our proposed hybrid variants and show that the addition of quantum layers does provide a noticeable computational advantage.

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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