Performance and energy efficiency analysis of a Reversi player for FPGAs and General Purpose Processors

Board-game applications are frequently found in mobile devices where the computing performance and the energy budget are constrained. Since the Artificial Intelligence techniques applied in these games are computationally intensive, the applications developed for mobile systems are frequently simplistic, far from the level of equivalent applications developed for desktop computers. Currently board games are software applications executed on General Purpose Processors. However, they exhibit a medium degree of parallelism and a custom hardware accelerator implemented on an FPGA can take advantage of that. We have selected the well-known Reversi game as a case study because it is a very popular board game with simple rules but huge computational demands. We developed and optimized software and hardware designs for this game that apply the same classical Artificial Intelligence techniques. The applications have been executed on different representative platforms and the results demonstrate that the FPGAs implementations provide better performance, lower power consumption and, therefore, impressive energy savings. These results demonstrate that FPGAs can efficiently deal with this kind of problems

Diseño e implementación de un jugador artificial de Reversi sobre una FPGA

El Field-Programmable Technology Design Competition es un concurso de diseño hardware internacional enmarcado en el International Conference on Field-Programmable Technology, congreso internacional de la región asiática sobre hardware reconfigurable. En su edición de 2010 propuso el desarrollo de un procesador específico para jugar al Reversi sobre una FPGA. Partiendo de conocimientos nulos acerca de la estrategia subyacente al juego, diseñamos e implementamos en 4 meses un procesador muy superior al software de referencia que suministraba la organización del concurso. El procesador implementa el algoritmo MinMax con poda alfa-beta, búsqueda en profundidad iterativa y ordenación dinámica de nodos para la exploración del espacio de búsqueda, y una evaluación de nodos basada en conceptos fuertemente ligados a la estrategia del juego, tales como movilidad, captura de esquinas o casillas estables. Posteriormente, desarrollamos una versión software algorítmicamente equivalente con el propósito de establecer comparativas de rendimiento y de consumo FPGA/PC. Los resultados muestran un mayor rendimiento del diseño hardware, fruto principalmente de la explotación del paralelismo y del diseño de una arquitectura a medida, y un consumo sustancialmente inferior, debido principalmente a que el procesador desarrollado trabaja a una frecuencia dos órdenes de magnitud inferior al PC. Como contrapartida, el tiempo de desarrollo del diseño hardware fue claramente superior que el del diseño software equivalente. El diseño presentado en la sesión del congreso dedicada a la competición fue capaz de batir al resto de finalistas, y por ello fuimos galardonados con el primer premio de la competición. Además, el artículo describiendo el diseño fue publicado en las actas del congreso, siendo accesible a la comunidad científica a través del IEEExplore

FPGAタブレットを用いた人工知能アプリケーションの高速化

　本研究では、FPGAを用いたAndroidタブレット端末を用いることで、2種類の人工知能アプリケーションの高速化を行った。　近年、スマートフォンなどの携帯型端末の普及や、利用方法が多様化したことにより、アプリケーション自体の機能や性能への需要も高まってきている。一方で、計算機そのものの性能が向上したことにより人工知能分野の研究が盛んになってきている。人工知能に関する研究分野には、囲碁や将棋、ポーカーなどのAI(Artificial Intelligence)、Deep Learning という多層で構成されたニューラルネットワークによる機械学習方法がある。これらの分野において、それ自体のアルゴリズムの改良はもちろん以前までは現実的でなかった計算量を処理することができるようになったことにより、その性能は飛躍的に向上してきている。　本研究では、これらの処理を行う際に必要となるアクセラレータとしてFPGAを選択し、そのFPGAをベースに作成されたAndroidタブレット(FPGA タブレット)を用いることで、これらの人工知能技術を含んだ携帯端末用アプリケーションの高速化を行った。FPGAタブレットは、FPGAのチップ上のARM コアでAndroid OSを動作させることにより、タブレット端末として利用できる。また、パーシャルリコンフィギュレーション機能を用いることで、FPGA の回路の一部をOS 動作中に書き換えることが可能である。これにより、動かすアプリケーションごとに専用回路を用意し、それらを必要に応じて書き換えていくことでアプリケーション全体の高速化を可能としている。本研究で作成したオセロアプリのAIでは、CPUと比較して平均約2倍程度の高速化、Deep Learningを用いた画像分類アプリでは、本研究で用いるタブレットシステムの資源等を考慮したモデル式の結果で約32～72倍の高速化の可能性を示した。また、VivadoHLSを用いた高位合成によって生成された部分専用回路の場合、CPUと比較して平均2.9倍程度の高速化が望めることを示した。電気通信大学201

A Field Guide to Genetic Programming

xiv, 233 p. : il. ; 23 cm.Libro ElectrónicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation, Initialisation and GP 2.1 Representation 2.2 Initialising the Population 2.3 Selection 2.4 Recombination and Mutation Operators in Tree-based 3 Getting Ready to Run Genetic Programming 19 3.1 Step 1: Terminal Set 19 3.2 Step 2: Function Set 20 3.2.1 Closure 21 3.2.2 Sufficiency 23 3.2.3 Evolving Structures other than Programs 23 3.3 Step 3: Fitness Function 24 3.4 Step 4: GP Parameters 26 3.5 Step 5: Termination and solution designation 27 4 Example Genetic Programming Run 4.1 Preparatory Steps 29 4.2 Step-by-Step Sample Run 31 4.2.1 Initialisation 31 4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming 5 Alternative Initialisations and Operators in 5.1 Constructing the Initial Population 5.1.1 Uniform Initialisation 5.1.2 Initialisation may Affect Bloat 5.1.3 Seeding 5.2 GP Mutation 5.2.1 Is Mutation Necessary? 5.2.2 Mutation Cookbook 5.3 GP Crossover 5.4 Other Techniques 32 5.5 Tree-based GP 39 6 Modular, Grammatical and Developmental Tree-based GP 47 6.1 Evolving Modular and Hierarchical Structures 47 6.1.1 Automatically Defined Functions 48 6.1.2 Program Architecture and Architecture-Altering 50 6.2 Constraining Structures 51 6.2.1 Enforcing Particular Structures 52 6.2.2 Strongly Typed GP 52 6.2.3 Grammar-based Constraints 53 6.2.4 Constraints and Bias 55 6.3 Developmental Genetic Programming 57 6.4 Strongly Typed Autoconstructive GP with PushGP 59 7 Linear and Graph Genetic Programming 61 7.1 Linear Genetic Programming 61 7.1.1 Motivations 61 7.1.2 Linear GP Representations 62 7.1.3 Linear GP Operators 64 7.2 Graph-Based Genetic Programming 65 7.2.1 Parallel Distributed GP (PDGP) 65 7.2.2 PADO 67 7.2.3 Cartesian GP 67 7.2.4 Evolving Parallel Programs using Indirect Encodings 68 8 Probabilistic Genetic Programming 8.1 Estimation of Distribution Algorithms 69 8.2 Pure EDA GP 71 8.3 Mixing Grammars and Probabilities 74 9 Multi-objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate 76 9.2.1 Multi-objective Bloat and Complexity Control 77 9.2.2 Other Objectives 78 9.2.3 Non-Pareto Criteria 80 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80 9.4 Multi-objective Optimisation via Operator Bias 81 10 Fast and Distributed Genetic Programming 83 10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83 10.2 Reducing Cost of Fitness with Caches 86 10.3 Parallel and Distributed GP are Not Equivalent 88 10.4 Running GP on Parallel Hardware 89 10.4.1 Master–slave GP 89 10.4.2 GP Running on GPUs 90 10.4.3 GP on FPGAs 92 10.4.4 Sub-machine-code GP 93 10.5 Geographically Distributed GP 93 11 GP Theory and its Applications 97 11.1 Mathematical Models 98 11.2 Search Spaces 99 11.3 Bloat 101 11.3.1 Bloat in Theory 101 11.3.2 Bloat Control in Practice 104 III Practical Genetic Programming 12 Applications 12.1 Where GP has Done Well 12.2 Curve Fitting, Data Modelling and Symbolic Regression 12.3 Human Competitive Results – the Humies 12.4 Image and Signal Processing 12.5 Financial Trading, Time Series, and Economic Modelling 12.6 Industrial Process Control 12.7 Medicine, Biology and Bioinformatics 12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii 12.9 Entertainment and Computer Games 127 12.10The Arts 127 12.11Compression 128 13 Troubleshooting GP 13.1 Is there a Bug in the Code? 13.2 Can you Trust your Results? 13.3 There are No Silver Bullets 13.4 Small Changes can have Big Effects 13.5 Big Changes can have No Effect 13.6 Study your Populations 13.7 Encourage Diversity 13.8 Embrace Approximation 13.9 Control Bloat 13.10 Checkpoint Results 13.11 Report Well 13.12 Convince your Customers 14 Conclusions Tricks of the Trade A Resources A.1 Key Books A.2 Key Journals A.3 Key International Meetings A.4 GP Implementations A.5 On-Line Resources 145 B TinyGP 151 B.1 Overview of TinyGP 151 B.2 Input Data Files for TinyGP 153 B.3 Source Code 154 B.4 Compiling and Running TinyGP 162 Bibliography 167 Inde

Suffolk University Undergraduate Academic Catalog, College of Arts and Sciences and Sawyer Business School, 2015-2016

This catalog contains information for the undergraduate programs in the College of Arts and Sciences and the Sawyer Business School. The catalog is a PDF version of the Suffolk website, so many pages have repeated information and links in the document will not work. The catalog is keyword searchable by clicking ctrl+f. A-Z course descriptions are also included here as separate PDF files with lists of CAS and SBS courses. Please contact the Archives if you need assistance navigating this catalog or finding information on degree requirements or course descriptions.https://dc.suffolk.edu/cassbs-catalogs/1170/thumbnail.jp

Suffolk University Undergraduate Academic Catalog, College of Arts and Sciences, 2016-2017

This catalog contains information for the undergraduate programs in the College of Arts and Sciences. The catalog is a PDF version of the Suffolk website, so many pages have repeated information and links in the document will not work. The catalog is keyword searchable by clicking ctrl+f. A-Z course descriptions are also included here as a separate PDF file listing all CAS course offerings. Please contact the Archives if you need assistance navigating this catalog or finding information on degree requirements or course descriptions.https://dc.suffolk.edu/cassbs-catalogs/1172/thumbnail.jp

Suffolk University Undergraduate Academic Catalog, College of Arts and Sciences, 2017-2018

This catalog contains information for the undergraduate programs in the College of Arts and Sciences. The catalog is a PDF version of the Suffolk website, so many pages have repeated information and links in the document will not work. The catalog is keyword searchable by clicking ctrl+f. A-Z course descriptions are also included here as a separate PDF file listing all CAS course offerings. Please contact the Archives if you need assistance navigating this catalog or finding information on degree requirements or course descriptions.https://dc.suffolk.edu/cassbs-catalogs/1175/thumbnail.jp

Suffolk University Academic Catalog, College of Arts and Sciences and Sawyer Business School, 2019-2020

This catalog contains information for the undergraduate programs in the College of Arts and Sciences and the Sawyer Business School. The catalog is a captured pdf version of the Suffolk website, so some pages have repeated information and many links in the document will not work. The catalog is keyword searchable by clicking ctrl+f. A-Z course descriptions are also included, with lists of CAS and SBS courses starting on page 1258. Please contact the Archives if you need assistance navigating this catalog or finding information on degree requirements or course descriptions.https://dc.suffolk.edu/cassbs-catalogs/1181/thumbnail.jp