Zobrazeno 1 - 10
of 110
pro vyhledávání: '"Sastry, Kumara"'
Autor:
Liu, Mingjie, Yang, Haoyu, Li, Zongyi, Sastry, Kumara, Mukhopadhyay, Saumyadip, Dogru, Selim, Anandkumar, Anima, Pan, David Z., Khailany, Brucek, Ren, Haoxing
Lithography modeling is a crucial problem in chip design to ensure a chip design mask is manufacturable. It requires rigorous simulations of optical and chemical models that are computationally expensive. Recent developments in machine learning have
Externí odkaz:
http://arxiv.org/abs/2210.15765
Autor:
Yang, Haoyu, Li, Zongyi, Sastry, Kumara, Mukhopadhyay, Saumyadip, Anandkumar, Anima, Khailany, Brucek, Singh, Vivek, Ren, Haoxing
Machine learning techniques have been extensively studied for mask optimization problems, aiming at better mask printability, shorter turnaround time, better mask manufacturability, and so on. However, most of these researches are focusing on the ini
Externí odkaz:
http://arxiv.org/abs/2207.04056
Autor:
Yang, Haoyu, Li, Zongyi, Sastry, Kumara, Mukhopadhyay, Saumyadip, Kilgard, Mark, Anandkumar, Anima, Khailany, Brucek, Singh, Vivek, Ren, Haoxing
Lithography simulation is a critical step in VLSI design and optimization for manufacturability. Existing solutions for highly accurate lithography simulation with rigorous models are computationally expensive and slow, even when equipped with variou
Externí odkaz:
http://arxiv.org/abs/2203.08616
Autor:
Sastry, Kumara Narasimha.
Thesis (Ph. D.)--University of Illinois at Urbana-Champaign, 2007.
Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7640. Advisers: David E. Goldberg; Duane D. Johnson. Includes bibliographical references (leaves 18
Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7640. Advisers: David E. Goldberg; Duane D. Johnson. Includes bibliographical references (leaves 18
Publikováno v:
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2008), ACM Press, 455-462
This paper proposes the incremental Bayesian optimization algorithm (iBOA), which modifies standard BOA by removing the population of solutions and using incremental updates of the Bayesian network. iBOA is shown to be able to learn and exploit unres
Externí odkaz:
http://arxiv.org/abs/0801.3113
The paper analyzes the scalability of multiobjective estimation of distribution algorithms (MOEDAs) on a class of boundedly-difficult additively-separable multiobjective optimization problems. The paper illustrates that even if the linkage is correct
Externí odkaz:
http://arxiv.org/abs/cs/0502057
This paper describes a scalable algorithm for solving multiobjective decomposable problems by combining the hierarchical Bayesian optimization algorithm (hBOA) with the nondominated sorting genetic algorithm (NSGA-II) and clustering in the objective
Externí odkaz:
http://arxiv.org/abs/cs/0502034
This paper discusses scalability of standard genetic programming (GP) and the probabilistic incremental program evolution (PIPE). To investigate the need for both effective mixing and linkage learning, two test problems are considered: ORDER problem,
Externí odkaz:
http://arxiv.org/abs/cs/0502029
This paper presents two different efficiency-enhancement techniques for probabilistic model building genetic algorithms. The first technique proposes the use of a mutation operator which performs local search in the sub-solution neighborhood identifi
Externí odkaz:
http://arxiv.org/abs/cs/0405062
Autor:
Sastry, Kumara, Goldberg, David E.
This paper presents a competent selectomutative genetic algorithm (GA), that adapts linkage and solves hard problems quickly, reliably, and accurately. A probabilistic model building process is used to automatically identify key building blocks (BBs)
Externí odkaz:
http://arxiv.org/abs/cs/0405064