Multidisciplinary heat generating logic block placement optimization using genetic algorithm

Suwa, Tohru; Hadim, Hamid

doi:10.1016/j.mejo.2008.01.087

Cited by 9 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the main objectives of PCB design is to reduce the size of the PCB as well as the temperature distribution of the components in order to have a longer lifecycle, improving performance and reliability. In this paper, we consider four main variables which are considered to have a lot of effect on the design of the component placement of the PCB (Bailey 2003;Bechtold et al 2005;Suwa and Hadim 2008). These variables which are the temperature of each component f (T ), area of PCB f (A), high power component placement f (P) and high potential critical components f (L) are formed as objective functions which govern the fitness function F(T, A, P, L).…”

Section: Multi-objective Problems Formulationmentioning

confidence: 99%

“…In this work, we use resistance thermal network for prediction of junction temperature of each component and thermal interconnections of components on PCB (Ammous et al 2003;Lee 2003;Vassighi and Sachdev 2006;Vellvehi et al 2007;Zare˛bski and Gōrecki 2007;Suwa and Hadim 2008). Thermal fitness of each component is defined as follows:…”

Section: Temperature Of Each Component F (T )mentioning

confidence: 99%

See 1 more Smart Citation

Optimization of electronics component placement design on PCB using self organizing genetic algorithm (SOGA)

2010

View full text Add to dashboard Cite

The optimal placement of electronic components on a printed circuit board (PCB) requires satisfying multiple conflicting design objectives as most of the components have different power dissipation, operating temperature, types of material and dimension. In addition, most electronic companies are currently emphasizing on designing a smaller package electronic system in order to increase the system performance. This paper presents a new self organizing genetic algorithm (SOGA) method for solving this multi-objective optimization problem. The SOGA can be viewed as a cascade of two GAs which consists of two steps fitness evaluation process to ensure that the fitness of selected chromosomes for each iteration process is optimally selected. The algorithm is developed based on weighted sum approach genetic algorithm (WSGA) where an inner loop GA is used to optimize the selection of weights of the WSGA. Experiments are conducted to evaluate the performance of SOGA. Four objective functions are formulated in the experiments which are temperature of components, area of PCB, high power component placement and high potential critical components distance. Comparisons of the performance of SOGA are made with two well known methods namely fixed weight GA (FWGA) and random weighted GA (RWGA). The results show that the SOGA gives a better optimal solution as compared to the other methods.

show abstract

Section: Multi-objective Problems Formulationmentioning

confidence: 99%

Section: Temperature Of Each Component F (T )mentioning

confidence: 99%

Optimization of electronics component placement design on PCB using self organizing genetic algorithm (SOGA)

2010

View full text Add to dashboard Cite

show abstract

“…Genetic Algorithms have been successfully used in a wide variety of applications such as pattern recognition and combinatorial optimization problems such as the traveling sales problem. In a recent study, Suwa and Hadim [13] presented the use of optimization methodology for placement of logic blocks on integrated circuit chips. Li et al [14] used multi-objective genetic algorithm for the optimization of thermal design of data center cabinets.…”

Section: Background On Genetic Algorithmmentioning

confidence: 99%

“…Genetic Algorithm techniques are based on the principles that crossing two individuals can result in offspring which are better than both parents, and that a slight mutation can also result in better individual. In the cross over process, the two parents are selected based on their fitness values and are modified to create new offspring [13]. In the mutation process, a member of the population is partially altered to introduce a random change in its characteristics.…”

Section: Background On Genetic Algorithmmentioning

confidence: 99%

Optimization of cluster cooling performance for data centers

Shrivastava¹,

VanGilder²,

Sammakia

2008

2008 11th Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems

View full text Add to dashboard Cite

A software tool using a Genetic Algorithm (GA) has been developed to optimize the cooling performance of a cluster of equipment comprised of two approximately-equal-length rows of racks and coolers bounding a common hot-aisle. Such clusters are "room neutral" from a cooling-load perspective if most or all of the hot rack exhaust is captured locally by the coolers. A direct cooling-performance assessment relative to this design objective is provided on a rack-level basis by the Capture Index (CI) and on a room-level basis by the Total Escaped Power (TEP). Various prediction techniques can be used to compute these cooling performance metrics for a cluster in real time. In this study, a Neural-Network-based algorithm is used as the "cooling prediction engine" upon which the GA optimization functionality is built. Typically, optimization of cluster cooling performance is attempted by intuition or trial and error, and one has to rely on the designer's experience. In contrast, the GA-based optimization technique in combination with the NN calculator provides a systematic approach for finding an optimized cluster layout. This integrated tool can be used to find the best arrangement of a fixed inventory of racks and coolers. This approach can also be used to find the best distribution of total heat load among racks and to identify the best rack location to which extra heat load can be added. Examples are discussed to demonstrate the potential of the software tool.

show abstract

“…These methods also do not integrate well with standard electrical design tools. This has led to the development and implementation of resistance-network based methods [11] and Green's functions based methods [10,12]. The resistance-based approach creates a thermal resistance network similar to an electrical resistance network and solves it for temperature.…”

Section: Introductionmentioning

confidence: 99%

Thermal characteristics of multi-die, three-dimensional integrated circuits with unequally sized die

Jain¹

2010

2010 12th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems

View full text Add to dashboard Cite

Three-dimensional integrated circuits (3D ICs) technology involves significant thermal management challenges due to the overlap of heat dissipation from several die. One particular problem is related to the thermal management of a multi-die stack with unequally-sized die. This is an important problem since several 3D IC process integration technologies requires unequally sized die. This paper investigates several thermal management and design issues in such a scenario. A resistance-network based model for a multi-die stack with unequally sized die is developed and validated using full finite-element simulations. In case all die dissipate the same power density, the model shows that as expected, it is thermally optimal to place the largest die nearest to the heat sink. On the other hand, if all die dissipate the same total power, the thermally optimal sequence calls for placing the two largest die at the two ends of the stack, in order to minimize the stack-to-package and stack-to-heatsink thermal resistances. In addition to these validation cases, the model enables prediction of temperature profiles for a general case with non-uniform die sizes, powers and power distributions, where the optimal sequence is not obvious. For example, in a somewhat surprising result, it is shown that the impact of choosing a thermally optimized stack sequence is somewhat diminished in the presence of hotspots on the die. It is shown that given the total silicon area, it is thermally optimal to partition the silicon area into equal-sized die, although this may be in conflict with the most economical option for manufacturing 3D ICs.

show abstract

Multidisciplinary heat generating logic block placement optimization using genetic algorithm

Cited by 9 publications

References 10 publications

Optimization of electronics component placement design on PCB using self organizing genetic algorithm (SOGA)

Optimization of electronics component placement design on PCB using self organizing genetic algorithm (SOGA)

Optimization of cluster cooling performance for data centers

Thermal characteristics of multi-die, three-dimensional integrated circuits with unequally sized die

Contact Info

Product

Resources

About