Optimization of FPGA-based CNN Accelerators Using Metaheuristics

In recent years, convolutional neural networks (CNNs) have demonstrated their ability to solve problems in many fields and with accuracy that was not possible before. However, this comes with extensive computational requirements, which made general CPUs unable to deliver the desired real-time performance. At the same time, FPGAs have seen a surge in interest for accelerating CNN inference. This is due to their ability to create custom designs with different levels of parallelism. Furthermore, FPGAs provide better performance per watt compared to GPUs. The current trend in FPGA-based CNN accelerators is to implement multiple convolutional layer processors (CLPs), each of which is tailored for a subset of layers. However, the growing complexity of CNN architectures makes optimizing the resources available on the target FPGA device to deliver optimal performance more challenging. In this paper, we present a CNN accelerator and an accompanying automated design methodology that employs metaheuristics for partitioning available FPGA resources to design a Multi-CLP accelerator. Specifically, the proposed design tool adopts simulated annealing (SA) and tabu search (TS) algorithms to find the number of CLPs required and their respective configurations to achieve optimal performance on a given target FPGA device. Here, the focus is on the key specifications and hardware resources, including digital signal processors, block RAMs, and off-chip memory bandwidth. Experimental results and comparisons using four well-known benchmark CNNs are presented demonstrating that the proposed acceleration framework is both encouraging and promising. The SA-/TS-based Multi-CLP achieves 1.31x - 2.37x higher throughput than the state-of-the-art Single-/Multi-CLP approaches in accelerating AlexNet, SqueezeNet 1.1, VGGNet, and GoogLeNet architectures on the Xilinx VC707 and VC709 FPGA boards.Comment: 23 pages, 7 figures, 9 tables. in The Journal of Supercomputing, 202

An empirical approach for currency identification

Currency identification is the application of systematic methods to determine authenticity of questioned currency. However, identification analysis is a difficult task requiring specially trained examiners, the most important challenge is automating the analysis process reducing human labor and time. In this study, an empirical approach for automated currency identification is formulated and a prototype is developed. A two parts feature vector is defined comprised of color features and texture features. Finally the banknote in question is classified by a Feedforward Neural Network (FNN) and a measurement of the similarity between existing samples and suspect banknote is output

Hybrid classification approach for imbalanced datasets

The research area of imbalanced dataset has been attracted increasing attention from both academic and industrial areas, because it poses a serious issues for so many supervised learning problems. Since the number of majority class dominates the number of minority class are from minority class, if training dataset includes all data in order to fit a classic classifier, the classifier tends to classify all data to majority class by ignoring minority data as noise. Thus, it is very significant to select appropriate training dataset in the prepossessing stage for classification of imbalanced dataset. We propose an combination approach of SMOTE (Synthetic Minority Over-sampling Technique) and instance selection approaches. The numeric results show that the proposed combination approach can help classifiers to achieve better performance

Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art

Procedural content generation (PCG) can be applied to a wide variety of tasks in games, from narratives, levels and sounds, to trees and weapons. A large amount of game content is comprised of graphical assets, such as clouds, buildings or vegetation, that do not require gameplay function considerations. There is also a breadth of literature examining the procedural generation of such elements for purposes outside of games. The body of research, focused on specific methods for generating specific assets, provides a narrow view of the available possibilities. Hence, it is difficult to have a clear picture of all approaches and possibilities, with no guide for interested parties to discover possible methods and approaches for their needs, and no facility to guide them through each technique or approach to map out the process of using them. Therefore, a systematic literature review has been conducted, yielding 200 accepted papers. This paper explores state-of-the-art approaches to graphical asset generation, examining research from a wide range of applications, inside and outside of games. Informed by the literature, a conceptual framework has been derived to address the aforementioned gaps

Seabed classification using physics-based modeling and machine learning

In this work model-based methods are employed along with machine learning techniques to classify sediments in oceanic environments based on the geoacoustic properties of a two-layer seabed. Two different scenarios are investigated. First, a simple low-frequency case is set up, where the acoustic field is modeled with normal modes. Four different hypotheses are made for seafloor sediment possibilities and these are explored using both various machine learning techniques and a simple matched-field approach. For most noise levels, the latter has an inferior performance to the machine learning methods. Second, the high-frequency model of the scattering from a rough, two-layer seafloor is considered. Again, four different sediment possibilities are classified with machine learning. For higher accuracy, 1D Convolutional Neural Networks (CNNs) are employed. In both cases we see that the machine learning methods, both in simple and more complex formulations, lead to effective sediment characterization. Our results assess the robustness to noise and model misspecification of different classifiers

Genetic-algorithm-optimized neural networks for gravitational wave classification

Gravitational-wave detection strategies are based on a signal analysis technique known as matched filtering. Despite the success of matched filtering, due to its computational cost, there has been recent interest in developing deep convolutional neural networks (CNNs) for signal detection. Designing these networks remains a challenge as most procedures adopt a trial and error strategy to set the hyperparameter values. We propose a new method for hyperparameter optimization based on genetic algorithms (GAs). We compare six different GA variants and explore different choices for the GA-optimized fitness score. We show that the GA can discover high-quality architectures when the initial hyperparameter seed values are far from a good solution as well as refining already good networks. For example, when starting from the architecture proposed by George and Huerta, the network optimized over the 20-dimensional hyperparameter space has 78% fewer trainable parameters while obtaining an 11% increase in accuracy for our test problem. Using genetic algorithm optimization to refine an existing network should be especially useful if the problem context (e.g. statistical properties of the noise, signal model, etc) changes and one needs to rebuild a network. In all of our experiments, we find the GA discovers significantly less complicated networks as compared to the seed network, suggesting it can be used to prune wasteful network structures. While we have restricted our attention to CNN classifiers, our GA hyperparameter optimization strategy can be applied within other machine learning settings.Comment: 25 pages, 8 figures, and 2 tables; Version 2 includes an expanded discussion of our hyperparameter optimization mode

Rancang bangun decision support system untuk clustering tingkat kerusakan bangunan pasca bencana alam menggunakan deep learning

Decision Support System (DSS) merupakan salah satu cabang keilmuan dari sistem informasi yang memiliki suatu intelligence. Menerapkan DSS untuk memecahkan suatu masalah merupakan satu bentuk riset yang banyak peneliti lakukan. Metode yang banyak di terapkan oleh para peneliti adalah Multi-Criteria Decision Making (MCDM), salah satu metode MCDM yaitu Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Salah satu kelemahan MCDM yaitu user harus melewati setiap langkah dari metode MCDM. Dengan adanya kelemahan tersebut maka peneliti melakukan kolaborasi dengan menerapkan Machine Learning (ML) pada DSS, tujuannya adalah agar DSS lebih cerdas karena user tidak perlu melakukan tahapan-tahapan DSS dalam memecahkan masalah. Pada penelitian kami menggunakan obyek untuk menentukan tingkat kerusakan sektor pasca bencana alam menggunakan Deep Learning (DL). Sebelum menerapkan metode DL yaitu Convutional Neural Network (CNN) untuk menentukan tingkat kerusakan sektor pasca bencana alam adalah melakukan pre-processing data. terdapat beberapa langkah dari pre-processing data diantaranya labeling data, dan augmentasi data. Dengan menggunakan data hasil dari DSS untuk mencari labeling data pada setiap data kerusakan sektor pasca bencana alam menggunakan Principal Component Analysis (PCA) agar pada saat melabelkan tingkat kerusakan sektor pasca bencana memiliki acuan secara ilmiah. Setelah mendapatkan labeling data tingkat kerusakan sektor pasca bencana alam menggunakan PCA kemudian menggunakan hasil reduksi parameter dari teknik PCA tersebut untuk acuan augmentasi gambar agar gambar dapat terbentuk sesuai dengan parameter yang digunakan. Kemudian hasil dari augmentasi gambar tersebut akan masuk proses watershed algoritm untuk mengetahui tingkat kerusakan sektor pasca bencana alam