38 research outputs found
A DPLL(T) Framework for Verifying Deep Neural Networks
Deep Neural Networks (DNNs) have emerged as an effective approach to tackling
real-world problems. However, like human-written software,
automatically-generated DNNs can have bugs and be attacked. This thus attracts
many recent interests in developing effective and scalable DNN verification
techniques and tools. In this work, we introduce a NeuralSAT, a new constraint
solving approach to DNN verification. The design of NeuralSAT follows the
DPLL(T) algorithm used modern SMT solving, which includes (conflict) clause
learning, abstraction, and theory solving, and thus NeuralSAT can be considered
as an SMT framework for DNNs. Preliminary results show that the NeuralSAT
prototype is competitive to the state-of-the-art. We hope, with proper
optimization and engineering, NeuralSAT will carry the power and success of
modern SAT/SMT solvers to DNN verification. NeuralSAT is avaliable from:
https://github.com/dynaroars/neuralsat-solverComment: 27 pages, 8 figures. NeuralSAT is avaliable from:
https://github.com/dynaroars/neuralsat-solve
FPGA-Based Acceleration of the Self-Organizing Map (SOM) Algorithm using High-Level Synthesis
One of the fastest growing and the most demanding areas of computer science is Machine Learning (ML). Self-Organizing Map (SOM), categorized as unsupervised ML, is a popular data-mining algorithm widely used in Artificial Neural Network (ANN) for mapping high dimensional data into low dimensional feature maps. SOM, being computationally intensive, requires high computational time and power when dealing with large datasets. Acceleration of many computationally intensive algorithms can be achieved using Field-Programmable Gate Arrays (FPGAs) but it requires extensive hardware knowledge and longer development time when employing traditional Hardware Description Language (HDL) based design methodology. Open Computing Language (OpenCL) is a standard framework for writing parallel computing programs that execute on heterogeneous computing systems. Intel FPGA Software Development Kit for OpenCL (IFSO) is a High-Level Synthesis (HLS) tool that provides a more efficient alternative to HDL-based design. This research presents an optimized OpenCL implementation of SOM algorithm on Stratix V and Arria 10 FPGAs using IFSO. Compared to recent SOM implementations on Central Processing Unit (CPU) and Graphics Processing Unit (GPU), our OpenCL implementation on FPGAs provides superior speed performance and power consumption results. Stratix V achieves speedup of 1.41x - 16.55x compared to AMD and Intel CPU and 2.18x compared to Nvidia GPU whereas Arria 10 achieves speedup of 1.63x - 19.15x compared to AMD and Intel CPU and 2.52x compared to Nvidia GPU. In terms of power consumption, Stratix V is 35.53x and 42.53x whereas Arria 10 is 15.82x and 15.93x more power efficient compared to CPU and GPU respectively
Learning SAT Encodings for Constraint Satisfaction Problems
Constraint programming addresses many interesting and challenging problems in
our world, including recent applications to contexts as diverse as allocating
refugee relief funds, short-term mine planning and hardware circuit design.
Users define their problems in high-level modelling languages which include
descriptive global constraints. One of the most effective ways to solve
constraint satisfaction problems (CSPs) is by translating them into instances
of the Boolean Satisfiability Problem (SAT). For some global constraints in
CSPs there exist many algorithms which encode the constraint into SAT;
choosing an appropriate SAT encoding can alter the ultimate solving time
dramatically.
We investigate the problem of selecting the best SAT encoding for
pseudo-Boolean and linear integer constraints. Many machine learning
techniques are explored, applied and evaluated to aid this selection. The
result is a significant improvement in performance compared to the default
choice and to the single best choice from a training set. The approach is
successful even for previously unseen problem classes and it greatly
outperforms a sophisticated general algorithm selection and configuration
tool.
This work provides a thorough empirical study and detailed analysis of each
stage in the machine learning process as applied to choosing SAT encodings.
It does this in three phases: firstly by using generic CSP instance features
to select an encoding per constraint type for each instance, then by
introducing new features which focus on the constraint types in question, and
finally by learning to select encodings for individual constraints.
We find that even generic instance features can produce good predictions, but
that the specialised features introduced give more robust performance
especially when predicting for unseen problem classes. Training to predict
per constraint shows potential and leads to better performance for some
problem classes, but per-instance selection is still competitive across the
corpus of problems as a whole
Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design – FMCAD 2021
The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing
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
Recent Trends in Communication Networks
In recent years there has been many developments in communication technology. This has greatly enhanced the computing power of small handheld resource-constrained mobile devices. Different generations of communication technology have evolved. This had led to new research for communication of large volumes of data in different transmission media and the design of different communication protocols. Another direction of research concerns the secure and error-free communication between the sender and receiver despite the risk of the presence of an eavesdropper. For the communication requirement of a huge amount of multimedia streaming data, a lot of research has been carried out in the design of proper overlay networks. The book addresses new research techniques that have evolved to handle these challenges
Tools and Algorithms for the Construction and Analysis of Systems
This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems
Computer Aided Verification
This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book