Multi-objective optimization techniques for VLSI circuits

Kashfi, Fatemeh; Hatami, Safar; Pedram, Massoud

doi:10.1109/isqed.2011.5770720

Cited by 31 publications

(9 citation statements)

References 6 publications

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“…In a multi-optimization framework, the objective function f (x)[f 1 (x), f 2 (x), , f n (x)] often conflicts with each other (Kashfi et al, 2011), such as the leakage power and delay in a circuit design. For conflicting objectives, it is not feasible to optimize the performance for all of them; improving one will result in deteriorating another.…”

Section: Pareto-optimalitymentioning

confidence: 99%

An efficient bi-objective optimization framework for statistical chip-level yield analysis under parameter variations

Sun

Xiao

et al. 2016

Frontiers Inf Technol Electronic Eng

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With shrinking technology, the increase in variability of process, voltage, and temperature (PVT) parameters significantly impacts the yield analysis and optimization for chip designs. Previous yield estimation algorithms have been limited to predicting either timing or power yield. However, neglecting the correlation between power and delay will result in significant yield loss. Most of these approaches also suffer from high computational complexity and long runtime. We suggest a novel bi-objective optimization framework based on Chebyshev affine arithmetic (CAA) and the adaptive weighted sum (AWS) method. Both power and timing yield are set as objective functions in this framework. The two objectives are optimized simultaneously to maintain the correlation between them. The proposed method first predicts the guaranteed probability bounds for leakage and delay distributions under the assumption of arbitrary correlations. Then a power-delay bi-objective optimization model is formulated by computation of cumulative distribution function (CDF) bounds. Finally, the AWS method is applied for power-delay optimization to generate a well-distributed set of Pareto-optimal solutions. Experimental results on ISCAS benchmark circuits show that the proposed bi-objective framework is capable of providing sufficient trade-off information between power and timing yield.

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Section: Pareto-optimalitymentioning

confidence: 99%

An efficient bi-objective optimization framework for statistical chip-level yield analysis under parameter variations

Sun

Xiao

et al. 2016

Frontiers Inf Technol Electronic Eng

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“…However, there are few papers e.g. Wang [35], Lu [36], and Kashfi [37]. A closer look into all the IEEE papers on FinFET SRAM will show that their approach is like fine-tuning the technology in order to ensure better customization of transistor charecteristics.…”

Section: Resultsmentioning

confidence: 99%

Insights of Performance Enhancement Techniques on FinFET-based SRAM Cells

Girish¹,

Shashikumar²

2016

CAE

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With the advancement in the energy efficient storage system, FinFET has already gained a pace in the area of computational memory management. However, after reviewing the research work focusing on FinFET based SRAM cells till date, we found that amount of research work towards enhancement of the design principle has not been much in number. Hence, we study some of the recently introduced research contribution towards enhancing the design performance of FinFET based SRAM cells and found that majority of the technique have both advantages and limitations too. We also highlights the significant research gap from the existing studies in order to assist the readers aware of the practicality of the research progress in this regards.

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“…The research field of considering decision problems with multiple conflicting objectives (goals or criteria) is known as multiple criteria decision-making or Multiobjective Optimization (MO) [27][28][29]. Such subject covers both discrete problems (with a finite set of alternatives, also called actions or solutions) and continuous problems.…”

Section: A Multiobjective Optimizationmentioning

confidence: 99%

Multiobjective Intelligent Energy Management for a Microgrid

Chaouachi

Kamel

Andoulsi

et al. 2013

IEEE Trans. Ind. Electron.

574

237

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In this paper, a generalized formulation for intelligent energy management of a microgrid is proposed using Artificial Intelligence (AI) techniques jointly with linear programming based multiobjective optimization. The proposed Multiobjective Intelligent Energy Management (MIEM) aims to minimize the operation cost and the environmental impact of a microgrid taking into account its pre-operational variables as future availability of renewable energies and load demand. An artificial Neural Network Ensemble (NNE) is developed to predict 24 hour ahead photovoltaic generation, one hour ahead wind power generation and load demand. The proposed machine learning is characterized by enhanced learning model and generalization capability. The efficiency of the microgrid operation strongly depends on the battery scheduling process, which cannot be achieved through conventional optimization formulation. In this study, a fuzzy logic expert system is used for battery scheduling. The proposed approach can handle uncertainties regarding to the fuzzy environment of the overall microgrid operation and the uncertainty related to the forecasted parameters. The results shows considerable minimization on operation cost and emission level comparing to literature microgrid energy management approaches based on opportunity charging and heuristic flowchart battery management. Index Terms-MultiobjectiveIntelligent Energy Management (MIEM), Neural Network Ensemble (NNE), Fuzzy Logic, Shortterm Forecasting, Microgrid.

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Multi-objective optimization techniques for VLSI circuits

Cited by 31 publications

References 6 publications

An efficient bi-objective optimization framework for statistical chip-level yield analysis under parameter variations

An efficient bi-objective optimization framework for statistical chip-level yield analysis under parameter variations

Insights of Performance Enhancement Techniques on FinFET-based SRAM Cells

Multiobjective Intelligent Energy Management for a Microgrid

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