NSGA-II Based Thermal-Aware Mixed Polarity Dual Reed–Muller Network Synthesis Using Parallel Tabular Technique

2020 ◽  
Vol 29 (15) ◽  
pp. 2020008
Author(s):  
Apangshu Das ◽  
Yallapragada C. Hareesh ◽  
Sambhu Nath Pradhan
Keyword(s):  
Power Density ◽  
Integrated Circuit ◽  
Absolute Temperature ◽  
Peak Temperature ◽  
Network Synthesis ◽  
Nsga Ii ◽  
Silicon Area ◽  
Mixed Polarity ◽  
Package Density ◽  
Nondominated Sorting

Proposed work presents an OR-XNOR-based thermal-aware synthesis approach to reduce peak temperature by eliminating local hotspots within a densely packed integrated circuit. Tremendous increase in package density at sub-nanometer technology leads to high power-density that generates high temperature and creates hotspots. A nonexhaustive meta-heuristic algorithm named nondominated sorting genetic algorithm-II (NSGA-II) has been employed for selecting suitable input polarity of mixed polarity dual Reed–Muller (MPDRM) expansion function to reduce the power-density. A parallel tabular technique is used for input polarity conversion from Product-of-Sum (POS) to MPDRM function. Without performance degradation, the proposed MPDRM approach shows more than 50% improvement in the area and power savings and around 6% peak temperature reduction for the MCNC benchmark circuits than that of earlier literature at the logic level. Algorithmic optimized circuit decompositions are implemented in physical design domain using CADENCE INNOVUS and HotSpot tool and silicon area, power consumption and absolute temperature are reported to validate the proposed technique.

scholarly journals Shared Reed-Muller Decision Diagram Based Thermal-Aware AND-XOR Decomposition of Logic Circuits

VLSI Design ◽  
2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Apangshu Das ◽  
Sambhu Nath Pradhan
Keyword(s):  
Power Density ◽  
Large Scale ◽  
Logic Synthesis ◽  
Absolute Temperature ◽  
Circuit Realization ◽  
Silicon Area ◽  
Trade Off ◽  
Density Reduction ◽  
Logic Minimization ◽  
Input Variables

The increased number of complex functional units exerts high power-density within a very-large-scale integration (VLSI) chip which results in overheating. Power-densities directly converge into temperature which reduces the yield of the circuit. An adverse effect of power-density reduction is the increase in area. So, there is a trade-off between area and power-density. In this paper, we introduce a Shared Reed-Muller Decision Diagram (SRMDD) based on fixed polarity AND-XOR decomposition to represent multioutput Boolean functions. By recursively applying transformations and reductions, we obtained a compact SRMDD. A heuristic based on Genetic Algorithm (GA) increases the sharing of product terms by judicious choice of polarity of input variables in SRMDD expansion and a suitable area and power-density trade-off has been enumerated. This is the first effort ever to incorporate the power-density as a measure of temperature estimation in AND-XOR expansion process. The results of logic synthesis are incorporated with physical design in CADENCE digital synthesis tool to obtain the floor-plan silicon area and power profile. The proposed thermal-aware synthesis has been validated by obtaining absolute temperature of the synthesized circuits using HotSpot tool. We have experimented with 29 benchmark circuits. The minimized AND-XOR circuit realization shows average savings up to 15.23% improvement in silicon area and up to 17.02% improvement in temperature over the sum-of-product (SOP) based logic minimization.

Thermal-aware Output Polarity Selection Based on And-Inverter Graph Manipulation

2019 ◽  
Vol 12 (1) ◽  
pp. 30-39 ◽  
Author(s):  
Apangshu Das ◽  
Sambhu Nath Pradhan
Keyword(s):  
Integrated Circuits ◽  
Power Density ◽  
Logic Synthesis ◽  
Programmable Logic Arrays ◽  
Power Switching ◽  
Nsga Ii ◽  
Multi Level ◽  
Search Approach ◽  
Efficient Power ◽  
The Impact

Background: Output polarity of the sub-function is generally considered to reduce the area and power of a circuit at the two-level realization. Along with area and power, the power-density is also one of the significant parameter which needs to be consider, because power-density directly converges to circuit temperature. More than 50% of the modern day integrated circuits are damaged due to excessive overheating. Methods: This work demonstrates the impact of efficient power density based logic synthesis (in the form of suitable polarity selection of sub-function of Programmable Logic Arrays (PLAs) for its multilevel realization) for the reduction of temperature. Two-level PLA optimization using output polarity selection is considered first and compared with other existing techniques and then And-Invert Graphs (AIG) based multi-level realization has been considered to overcome the redundant solution generated in two-level synthesis. AIG nodes and associated power dissipation can be reduced by rewriting, refactoring and balancing technique. Reduction of nodes leads to the reduction of the area but on the contrary increases power and power density of the circuit. A meta-heuristic search approach i.e., Nondominated Sorting Genetic Algorithm-II (NSGA-II) is proposed to select the suitable output polarity of PLA sub-functions for its optimal realization. Results: Best power density based solution saves up to 8.29% power density compared to ‘espresso – dopo’ based solutions. Around 9.57% saving in area and 9.67% saving in power (switching activity) are obtained with respect to ‘espresso’ based solution using NSGA-II. Conclusion: Suitable output polarity realized circuit is converted into multi-level AIG structure and synthesized to overcome the redundant solution at the two-level circuit. It is observed that with the increase in power density, the temperature of a particular circuit is also increases.

Shape optimization of egg-shaped sewer pipes based on the nondominated sorting genetic algorithm (NSGA-II)

2022 ◽  
Vol 204 ◽  
pp. 111999
Author(s):  
Hanting Wu ◽  
Yangrui Huang ◽  
Lei Chen ◽  
Yingjie Zhu ◽  
Huaizheng Li
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Nsga Ii ◽  
Sewer Pipes ◽  
Nondominated Sorting

scholarly journals Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
K. Vijayakumar
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Electricity Market ◽  
Optimization Methods ◽  
Evolutionary Programming ◽  
Congestion Management ◽  
Transmission Congestion ◽  
Nsga Ii ◽  
Line Overload ◽  
Nondominated Sorting

Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP) techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA) II gives a set of Pareto-optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.

scholarly journals Technical note: the caRamel R package for Automatic Calibration by Evolutionary Multi Objective Algorithm

2019 ◽  
Author(s):  
Céline Monteil ◽  
Fabrice Zaoui ◽  
Nicolas Le Moine ◽  
Frédéric Hendrickx
Keyword(s):  
Stable Solution ◽  
R Package ◽  
Technical Note ◽  
Main Function ◽  
Nsga Ii ◽  
Multiple Criteria Optimization ◽  
Multi Objective ◽  
Study Case ◽  
Nondominated Sorting

Abstract. Environmental modelling is complex, and models often require the calibration of several parameters that are not directly evaluable from a physical quantity or a field measurement. The R package caRamel has been designed to easily implement a multi-objective optimizer in the R environment to calibrate these parameters. A multiobjective calibration allows to find a compromise between different goals by defining a set of optimal parameters. The algorithm is a hybrid of the Multiobjective Evolutionary Annealing Simplex method (MEAS) and the Nondominated Sorting Genetic Algorithm II (ε-NSGA-II algorithm). The optimizer was initially developed for the calibration of hydrological models but can be used for any environmental model. The main function of the package, caRamel(), requires to define a multi-objective calibration function as well as bounds on the variation of the underlying parameters to optimize. CaRamel is well adapted to complex modelling. As an example, caRamel converges quickly and has a stable solution after 5,000 model evaluations with robust results for a real study case of a hydrological problem with 8 parameters and 3 objectives of calibration. The comparison with another well-known optimizer (i.e. MCO, for Multiple Criteria Optimization) confirms the quality of the algorithm.

Thermal-Aware Partitioning and Encoding of Power-Gated FSM

2019 ◽  
Vol 28 (09) ◽  
pp. 1950144
Author(s):  
Priyanka Choudhury ◽  
Kanchan Manna ◽  
Vivek Rai ◽  
Sambhu Nath Pradhan
Keyword(s):  
Power Density ◽  
Power Dissipation ◽  
Hot Spots ◽  
Cmos Technology ◽  
Absolute Temperature ◽  
Temperature Management ◽  
Average Temperature ◽  
Finite State ◽  
Suitable Area ◽  
Power Densities

Miniaturization and the continued scaling of CMOS technology leads to the high-power dissipation and ever-increasing power densities. One of the major challenges for the designer at all design levels is the temperature management, particularly the local hot spots along with power dissipation. In this work, the controller circuits which are implemented as Finite State Machines (FSMs) are considered for their thermal-aware and power-aware realization. Using Genetic Algorithm (GA), both encoding and bipartitioning of the FSM circuit are implemented to get two subFSMs such that at a particular instant of time, one subFSM is active at a time, whereas the other one is power-gated. Again, thermal-aware realization (in terms of power-density) of this power-gated FSM is done. Therefore, the work concerns with the thermal-aware encoding and partitioning of FSM for its power-gated realization. Average temperature saving obtained in this approach for a set of benchmark circuits over previous works is more than 16%. After getting the final partitioned circuit which is optimized in terms of Area and power-density, thermal analysis of the sunFSMs is performed to get the absolute temperature. As thermal-aware design may increase the area, a suitable area-temperature trade-off is also presented in this paper.

scholarly journals Fast Nondominated Sorting Genetic Algorithm II with Lévy Distribution for Network Topology Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Maoqing Zhang ◽  
Lei Wang ◽  
Zhihua Cui ◽  
Jiangshan Liu ◽  
Dong Du ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Topology Optimization ◽  
Network Topology ◽  
Optimization Problems ◽  
Selection Strategy ◽  
Nsga Ii ◽  
Comparison Results ◽  
Practical Engineering ◽  
Network Topology Optimization ◽  
Nondominated Sorting

Fast nondominated sorting genetic algorithm II (NSGA-II) is a classical method for multiobjective optimization problems and has exhibited outstanding performance in many practical engineering problems. However, the tournament selection strategy used for the reproduction in NSGA-II may generate a large amount of repetitive individuals, resulting in the decrease of population diversity. To alleviate this issue, Lévy distribution, which is famous for excellent search ability in the cuckoo search algorithm, is incorporated into NSGA-II. To verify the proposed algorithm, this paper employs three different test sets, including ZDT, DTLZ, and MaF test suits. Experimental results demonstrate that the proposed algorithm is more promising compared with the state-of-the-art algorithms. Parameter sensitivity analysis further confirms the robustness of the proposed algorithm. In addition, a two-objective network topology optimization model is then used to further verify the proposed algorithm. The practical comparison results demonstrate that the proposed algorithm is more effective in dealing with practical engineering optimization problems.

scholarly journals Evaluation of the Mass Diffusion Coefficient and Mass Biot Number Using a Nondominated Sorting Genetic Algorithm

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 260 ◽  
Author(s):  
Radosław Winiczenko ◽  
Krzysztof Górnicki ◽  
Agnieszka Kaleta
Keyword(s):  
Genetic Algorithm ◽  
Diffusion Coefficient ◽  
Biot Number ◽  
Moisture Diffusion ◽  
Absolute Error ◽  
Mass Diffusion ◽  
Precise Determination ◽  
Nsga Ii ◽  
Moisture Diffusion Coefficient ◽  
Nondominated Sorting

A precise determination of the mass diffusion coefficient and the mass Biot number is indispensable for deeper mass transfer analysis that can enable finding optimum conditions for conducting a considered process. The aim of the article is to estimate the mass diffusion coefficient and the mass Biot number by applying nondominated sorting genetic algorithm (NSGA) II genetic algorithms. The method is used in drying. The maximization of coefficient of correlation (R) and simultaneous minimization of mean absolute error (MAE) and root mean square error (RMSE) between the model and experimental data were taken into account. The Biot number and moisture diffusion coefficient can be determined using the following equations: Bi = 0.7647141 + 10.1689977s − 0.003400086T + 948.715758s2 + 0.000024316T2 − 0.12478256sT, D = 1.27547936∙10−7 − 2.3808∙10−5s − 5.08365633∙10−9T + 0.0030005179s2 + 4.266495∙10−11T2 + 8.33633∙10−7sT or Bi = 0.764714 + 10.1689091s − 0.003400089T + 948.715738s2 + 0.000024316T2 − 0.12478252sT, D = 1.27547948∙10−7 − 2.3806∙10−5s − 5.08365753∙10−9T + 0.0030005175s2 + 4.266493∙10−11T2 + 8.336334∙10−7sT. The results of statistical analysis for the Biot number and moisture diffusion coefficient equations were as follows: R = 0.9905672, MAE = 0.0406375, RMSE = 0.050252 and R = 0.9905611, MAE = 0.0406403 and RMSE = 0.050273, respectively.

scholarly journals A Scatter Search Approach for Multiobjective Selective Disassembly Sequence Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Xiwang Guo ◽  
Shixin Liu
Keyword(s):  
Multiobjective Optimization ◽  
Scatter Search ◽  
Nsga Ii ◽  
Disassembly Sequence ◽  
Selective Disassembly ◽  
Local Search Strategy ◽  
Search Approach ◽  
Nondominated Sorting ◽  
Disassembly Time

Disassembly sequence has received much attention in recent years. This work proposes a multiobjective optimization of model for selective disassembly sequences and maximizing disassembly profit and minimizing disassembly time. An improved scatter search (ISS) is adapted to solve proposed multiobjective optimization model, which embodies diversification generation of initial solutions, crossover combination operator, the local search strategy to improve the quality of new solutions, and reference set update method. To analyze the effect on the performance of ISS, simulation experiments are conducted on different products. The validity of ISS is verified by comparing the optimization effects of ISS and nondominated sorting genetic algorithm (NSGA-II).

Thermoeconomic Optimization and Comparison of Plate-Fin Heat Exchangers Using Louver, Offset Strip, Triangular and Rectangular Fins Applied in 200 kW Microturbines

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Peyman Maghsoudi ◽  
Sadegh Sadeghi ◽  
Pedram Hanafizadeh
Keyword(s):  
Heat Exchangers ◽  
Optimization Problem ◽  
Net Present Value ◽  
Optimal Solutions ◽  
Counter Flow ◽  
Nsga Ii ◽  
Triangular Fin ◽  
Offset Strip Fin ◽  
Cycle Efficiency ◽  
Nondominated Sorting

In this paper, four types of plate-fin heat exchangers applied in 200 kW microturbines are investigated. Multi-objective optimization algorithm, NSGA-II (nondominated sorting genetic algorithm (GA)), is employed to maximize the efficiency of the recuperator and minimize its total cost, simultaneously. Feasible ranges of pressure drop, Reynolds number, and recuperator efficiency are obtained according to a penalty function. The optimizations are conducted for rectangular fin, triangular fin, louver fin, and offset strip fin recuperators with cross and counter flow arrangements. The results of each optimization problem are presented as a set of designs, called “Pareto-optimal solutions.” Afterward, for the designs, cycle efficiency and net present value (NPV) are compared based on technical and economic criteria, respectively. Maximum cycle efficiency occurring in a recuperator with louver fin and counter flow arrangement is found to be 38.17%. Finally, the optimum designs are compared based on nondominated sorting concept leading to the optimal solutions.

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