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Automated Multi-Objective Parallel Evolutionary Circuit Design and Approximation
Hrbáček, Radek ; Fišer, Petr (referee) ; Trefzer,, Martin (referee) ; Sekanina, Lukáš (advisor)
Spotřeba a energetická efektivita se stává jedním z nejdůležitějších parametrů při návrhu počítačových systémů, zejména kvůli omezené kapacitě napájení u zařízení napájených bateriemi a velmi vysoké spotřebě energie rostoucích datacenter a cloudové infrastruktury. Současně jsou uživatelé ochotni do určité míry tolerovat nepřesné nebo chybné výpočty v roustoucím počtu aplikací díky nedokonalostem lidských smyslů, statistické povaze výpočtů, šumu ve vstupních datech apod. Přibližné počítání, nová oblast výzkumu v počítačovém inženýrství, využívá rozvolnění požadavků na funkčnost za účelem zvýšení efektivity počítačových systémů, pokud jde o spotřebu energie, výpočetní výkon či složitost. Aplikace tolerující chyby mohou být implementovány efektivněji a stále sloužit svému účelu se stejnou nebo mírně sníženou kvalitou. Ačkoli se objevují nové metody pro návrh přibližně počítajících výpočetních systémů, je stále nedostatek automatických návrhových metod, které by nabízely velké množství kompromisních řešení dané úlohy. Konvenční metody navíc často produkují řešení, která jsou daleko od optima. Evoluční algoritmy sice přinášejí inovativní řešení složitých optimalizačních a návrhových problémů, nicméně trpí několika nedostatky, např. nízkou škálovatelností či vysokým počtem generací nutných k dosažení konkurenceschopných výsledků. Pro přibližné počítání je vhodný zejména multikriteriální návrh, což existující metody většinou nepodporují. V této práci je představen nový automatický multikriteriální paralelní evoluční algoritmus pro návrh a aproximaci digitálních obvodů. Metoda je založena na kartézském genetickém programování, pro zvýšení škálovatelnosti byla navržena nová vysoce paralelizovaná implementace. Multikriteriální návrh byl založen na principech algoritmu NSGA-II. Výkonnost implementace byla vyhodnocena na několika různých úlohách, konkrétně při návrhu (přibližně počítajících) aritmetických obvodů, Booleovských funkcích s vysokou nelinearitou či přibližných logických obvodů pro tří-modulovou redundanci. V těchto úlohách bylo dosaženo význammých zlepšení ve srovnání se současnými metodami.
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Adaptive Plot Generation in Role-Playing Game
Vymazal, Jiří ; Grochol, David (referee) ; Hrbáček, Radek (advisor)
Generating a story, while trying to preserve at least some qualities of author-written narrative is a complex issue. In this thesis several currently existing systems and approaches are discussed. Then, a solution based on evolutionary computation is presented, and its traits shown on small-scale proof-of-concept scenario. Finally, this approach is compared againist existing solutions.
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Evolutionary Design of Filters for Signal Processing
Dobiš, Tomáš ; Hrbáček, Radek (referee) ; Dobai, Roland (advisor)
Kalman filter is used for signal filtering dependent on filter configuration and prediction of values. It's configuration is difficult and requires experiences of mathematician. This thesis deals with implementation of method for signal processing with use of Cartesian genetic programming, which advantage includes the automated configuration of filter. Final method is compared on multiple testing examples with Kalman filter. From results we can infer, that implemented method works comparatively efficient on periodic and exponential signal inputs, and works significantly better on constant signal inputs than Kalman filter.
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Coevolution of Image Filters and Fitness Predictors
Trefilík, Jakub ; Hrbáček, Radek (referee) ; Drahošová, Michaela (advisor)
This thesis deals with employing coevolutionary principles to the image filter design. Evolutionary algorithms are very advisable method for image filter design. Using coevolution, we can add the processes, which can accelerate the convergence by interactions of candidate filters population with population of fitness predictors. Fitness predictor is a small subset of the training set and it is used to approximate the fitness of the candidate solutions. In this thesis, indirect encoding is used for predictors evolution. This encoding represents a mathematical expression, which selects training vectors for candidate filters fitness prediction. This approach was experimentally evaluated in the task of image filters for various intensity of random impulse and salt and pepper noise design and the design of the edge detectors. It was shown, that this approach leads to adapting the number of target objective vectors for a particular task, which leads to computational complexity reduction.
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High Performance Applications on Intel Xeon Phi Cluster
Kačurik, Tomáš ; Hrbáček, Radek (referee) ; Jaroš, Jiří (advisor)
The main topic of this thesis is the implementation and subsequent optimization of high performance applications on a cluster of Intel Xeon Phi coprocessors. Using two approaches to solve the N-Body problem, the possibilities of the program execution on a cluster of processors, coprocessors or both device types have been demonstrated. Two particular versions of the N-Body problem have been chosen - the naive and Barnes-hut. Both problems have been implemented and optimized. For better comparison of the achieved results, we only considered achieved acceleration against single node runs using processors only. In the case of the naive version a 15-fold increase has been achieved when using combination of processors and coprocessors on 8 computational nodes. The performance in this case was 9 TFLOP/s. Based on the obtained results we concluded the advantages and disadvantages of the program execution in the distributed environments using processors, coprocessors or both.
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Evolutionary Design of Combinational Circuits on Computer Cluster
Pánek, Richard ; Zachariášová, Marcela (referee) ; Hrbáček, Radek (advisor)
This master's thesis deals with evolutionary algorithms and how them to use to design of combinational circuits. Genetic programming especially CGP is the most applicable to use for this type of task. Furthermore, it deals with computation on computer cluster and the use of evolutionary algorithms on them. For this computation is the most suited island models with CGP. Then a new way of recombination in CGP is designed to improve them. This design is implemented and tested on the computer cluster.
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Mutation in Cartesian Genetic Programming
Končal, Ondřej ; Hrbáček, Radek (referee) ; Wiglasz, Michal (advisor)
This thesis examines various kinds of mutations in the Cartesian Genetic Programming (CGP) on tasks of symbolic regression. The CGP is kind of evolutionary algorithm which operates with executable structures. Programs in CGP are evolved using mutation, which leads to offspring evaluation, which is the most time-consuming part of the algorithm. Finding more suitable kind of mutation can significantly accelerate the creation of new individuals and thus, reduce the time necessary to find a satisfactory solution. This thesis presents four different mutations for CGP. Experiments compare these mutation operators to solve five tasks of symbolic regression. Experiments have shown that a choice of suitable mutation can almost double the computing speed in comparison to the standard mutation.
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Coevolutionary Algorithm in FPGA
Hrbáček, Radek ; Vašíček, Zdeněk (referee) ; Drahošová, Michaela (advisor)
This thesis deals with the design of a hardware acceleration unit for digital image filter design using coevolutionary algorithms. The first part introduces reconfigurable logic device technology that the acceleration unit is based on. The theoretical part also briefly characterizes evolutionary and coevolutionary algorithms, their principles and applications. Traditional image filter designs are compared with the biologically inspired design methods. The hardware unit presented in this thesis exploits dual MicroBlaze system extended by custom peripherals to accelerate cartesian genetic programming. The coevolutionary image filter design is accelerated up to 58 times. The hardware platform functionality in the task of impulse noise filter design and edge detector design has been empirically analyzed.
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Dynamic Approximation of Digital Circuits
Jásenský, Michal ; Hrbáček, Radek (referee) ; Sekanina, Lukáš (advisor)
This bachelor's thesis deals with design of a method based on cartesian genetic programming, which allows the evolutionary design of circuits capable of dynamic reconfiguration. The goal of reconfiguration is to dynamically change the number of used components and thereby to change the accuracy of calculation. In this thesis, implementation of the proposed method is described. The method is experimentally verified and demonstrated on several selected circuits.
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