399 research outputs found

    A committee machine gas identification system based on dynamically reconfigurable FPGA

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    This paper proposes a gas identification system based on the committee machine (CM) classifier, which combines various gas identification algorithms, to obtain a unified decision with improved accuracy. The CM combines five different classifiers: K nearest neighbors (KNNs), multilayer perceptron (MLP), radial basis function (RBF), Gaussian mixture model (GMM), and probabilistic principal component analysis (PPCA). Experiments on real sensors' data proved the effectiveness of our system with an improved accuracy over individual classifiers. Due to the computationally intensive nature of CM, its implementation requires significant hardware resources. In order to overcome this problem, we propose a novel time multiplexing hardware implementation using a dynamically reconfigurable field programmable gate array (FPGA) platform. The processing is divided into three stages: sampling and preprocessing, pattern recognition, and decision stage. Dynamically reconfigurable FPGA technique is used to implement the system in a sequential manner, thus using limited hardware resources of the FPGA chip. The system is successfully tested for combustible gas identification application using our in-house tin-oxide gas sensors

    Efficient FPGA implementation and power modelling of image and signal processing IP cores

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    Field Programmable Gate Arrays (FPGAs) are the technology of choice in a number ofimage and signal processing application areas such as consumer electronics, instrumentation, medical data processing and avionics due to their reasonable energy consumption, high performance, security, low design-turnaround time and reconfigurability. Low power FPGA devices are also emerging as competitive solutions for mobile and thermally constrained platforms. Most computationally intensive image and signal processing algorithms also consume a lot of power leading to a number of issues including reduced mobility, reliability concerns and increased design cost among others. Power dissipation has become one of the most important challenges, particularly for FPGAs. Addressing this problem requires optimisation and awareness at all levels in the design flow. The key achievements of the work presented in this thesis are summarised here. Behavioural level optimisation strategies have been used for implementing matrix product and inner product through the use of mathematical techniques such as Distributed Arithmetic (DA) and its variations including offset binary coding, sparse factorisation and novel vector level transformations. Applications to test the impact of these algorithmic and arithmetic transformations include the fast Hadamard/Walsh transforms and Gaussian mixture models. Complete design space exploration has been performed on these cores, and where appropriate, they have been shown to clearly outperform comparable existing implementations. At the architectural level, strategies such as parallelism, pipelining and systolisation have been successfully applied for the design and optimisation of a number of cores including colour space conversion, finite Radon transform, finite ridgelet transform and circular convolution. A pioneering study into the influence of supply voltage scaling for FPGA based designs, used in conjunction with performance enhancing strategies such as parallelism and pipelining has been performed. Initial results are very promising and indicated significant potential for future research in this area. A key contribution of this work includes the development of a novel high level power macromodelling technique for design space exploration and characterisation of custom IP cores for FPGAs, called Functional Level Power Analysis and Modelling (FLPAM). FLPAM is scalable, platform independent and compares favourably with existing approaches. A hybrid, top-down design flow paradigm integrating FLPAM with commercially available design tools for systematic optimisation of IP cores has also been developed.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    SYSTEM-ON-A-CHIP (SOC)-BASED HARDWARE ACCELERATION FOR HUMAN ACTION RECOGNITION WITH CORE COMPONENTS

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    Today, the implementation of machine vision algorithms on embedded platforms or in portable systems is growing rapidly due to the demand for machine vision in daily human life. Among the applications of machine vision, human action and activity recognition has become an active research area, and market demand for providing integrated smart security systems is growing rapidly. Among the available approaches, embedded vision is in the top tier; however, current embedded platforms may not be able to fully exploit the potential performance of machine vision algorithms, especially in terms of low power consumption. Complex algorithms can impose immense computation and communication demands, especially action recognition algorithms, which require various stages of preprocessing, processing and machine learning blocks that need to operate concurrently. The market demands embedded platforms that operate with a power consumption of only a few watts. Attempts have been mad to improve the performance of traditional embedded approaches by adding more powerful processors; this solution may solve the computation problem but increases the power consumption. System-on-a-chip eld-programmable gate arrays (SoC-FPGAs) have emerged as a major architecture approach for improving power eciency while increasing computational performance. In a SoC-FPGA, an embedded processor and an FPGA serving as an accelerator are fabricated in the same die to simultaneously improve power consumption and performance. Still, current SoC-FPGA-based vision implementations either shy away from supporting complex and adaptive vision algorithms or operate at very limited resolutions due to the immense communication and computation demands. The aim of this research is to develop a SoC-based hardware acceleration workflow for the realization of advanced vision algorithms. Hardware acceleration can improve performance for highly complex mathematical calculations or repeated functions. The performance of a SoC system can thus be improved by using hardware acceleration method to accelerate the element that incurs the highest performance overhead. The outcome of this research could be used for the implementation of various vision algorithms, such as face recognition, object detection or object tracking, on embedded platforms. The contributions of SoC-based hardware acceleration for hardware-software codesign platforms include the following: (1) development of frameworks for complex human action recognition in both 2D and 3D; (2) realization of a framework with four main implemented IPs, namely, foreground and background subtraction (foreground probability), human detection, 2D/3D point-of-interest detection and feature extraction, and OS-ELM as a machine learning algorithm for action identication; (3) use of an FPGA-based hardware acceleration method to resolve system bottlenecks and improve system performance; and (4) measurement and analysis of system specications, such as the acceleration factor, power consumption, and resource utilization. Experimental results show that the proposed SoC-based hardware acceleration approach provides better performance in terms of the acceleration factor, resource utilization and power consumption among all recent works. In addition, a comparison of the accuracy of the framework that runs on the proposed embedded platform (SoCFPGA) with the accuracy of other PC-based frameworks shows that the proposed approach outperforms most other approaches

    MLP neural network based gas classification system on Zynq SoC

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    Systems based on Wireless Gas Sensor Networks (WGSN) offer a powerful tool to observe and analyse data in complex environments over long monitoring periods. Since the reliability of sensors is very important in those systems, gas classification is a critical process within the gas safety precautions. A gas classification system has to react fast in order to take essential actions in case of fault detection. This paper proposes a low latency real-time gas classification service system, which uses a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) to detect and classify the gas sensor data. An accurate MLP is developed to work with the data set obtained from an array of tin oxide (SnO2) gas sensor, based on convex Micro hotplates (MHP). The overall system acquires the gas sensor data through RFID, and processes the sensor data with the proposed MLP classifier implemented on a System on Chip (SoC) platform from Xilinx. Hardware implementation of the classifier is optimized to achieve very low latency for real-time application. The proposed architecture has been implemented on a ZYNQ SoC using fixed-point format and achieved results have shown that an accuracy of 97.4% has been obtained

    A Fully-Pipelined Hardware Design for Gaussian Mixture Models

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    Gaussian Mixture Models (GMMs) are widely used in many applications such as data mining, signal processing and computer vision, for probability density modeling and soft clustering. However, the parameters of a GMM need to be estimated from data by, for example, the Expectation-Maximization algorithm for Gaussian Mixture Models (EM-GMM), which is computationally demanding. This paper presents a novel design for the EM-GMM algorithm targeting reconfigurable platforms, with five main contributions. First, a pipeline-friendly EM-GMM with diagonal covariance matrices that can easily be mapped to hardware architectures. Second, a function evaluation unit for Gaussian probability density based on fixed-point arithmetic. Third, our approach is extended to support a wide range of dimensions or/and components by fitting multiple pieces of smaller dimensions onto an FPGA chip. Fourth, we derive a cost and performance model that estimates logic resources. Fifth, our dataflow design targeting the Maxeler MPCX2000 with a Stratix-5SGSD8 FPGA can run over 200 times faster than a 6-core Xeon E5645 processor, and over 39 times faster than a Pascal TITAN-X GPU. Our design provides a practical solution to applications for training and explores better parameters for GMMs with hundreds of millions of high dimensional input instances, for low-latency and high-performance applications

    Intrinsically Evolvable Artificial Neural Networks

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    Dedicated hardware implementations of neural networks promise to provide faster, lower power operation when compared to software implementations executing on processors. Unfortunately, most custom hardware implementations do not support intrinsic training of these networks on-chip. The training is typically done using offline software simulations and the obtained network is synthesized and targeted to the hardware offline. The FPGA design presented here facilitates on-chip intrinsic training of artificial neural networks. Block-based neural networks (BbNN), the type of artificial neural networks implemented here, are grid-based networks neuron blocks. These networks are trained using genetic algorithms to simultaneously optimize the network structure and the internal synaptic parameters. The design supports online structure and parameter updates, and is an intrinsically evolvable BbNN platform supporting functional-level hardware evolution. Functional-level evolvable hardware (EHW) uses evolutionary algorithms to evolve interconnections and internal parameters of functional modules in reconfigurable computing systems such as FPGAs. Functional modules can be any hardware modules such as multipliers, adders, and trigonometric functions. In the implementation presented, the functional module is a neuron block. The designed platform is suitable for applications in dynamic environments, and can be adapted and retrained online. The online training capability has been demonstrated using a case study. A performance characterization model for RC implementations of BbNNs has also been presented

    Design of hardware architectures for HMM–based signal processing systems with applications to advanced human-machine interfaces

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    In questa tesi viene proposto un nuovo approccio per lo sviluppo di interfacce uomo–macchina. In particolare si tratta il caso di sistemi di pattern recognition che fanno uso di Hidden Markov Models per la classificazione. Il progetto di ricerca è partito dall’ideazione di nuove tecniche per la realizzazione di sistemi di riconoscimento vocale per parlato spontaneo. Gli HMM sono stati scelti come lo strumento algoritmico di base per la realizzazione del sistema. Dopo una fase di studio preliminare gli obiettivi sono stati estesi alla realizzazione di una architettura hardware in grado di fornire uno strumento riconfigurabile che possa essere utilizzato non solo per il riconoscimento vocale, ma in qualsiasi tipo di classificatore basato su HMM. Il lavoro si concentra quindi sullo sviluppo di architetture hardware dedicate, ma nuovi risultati sono stati ottenuti anche a livello di applicazione per quanto riguarda la classificazione di segnali elettroencefalografici attraverso gli HMM. Innanzitutto state sviluppata una architettura a livello di sistema applicabile a qualsiasi sistema di pattern recognition che faccia usi di HMM. L’architettura stata concepita in modo tale da essere utilizzabile come un sistema stand–alone. Definita l’architettura, un processore hardware per HMM, completamente riconfigurabile, stato decritto in linguaggio VHDL e simulato con successo. Un array parallelo di questi processori costituisce di fatto il nucleo di processamento dell’architettura sviluppata. Sulla base del progetto in VHDL, due piattaforme di prototipaggio rapido basate su FPGA sono state selezionate per dei test di implementazione. Diverse configurazioni costituite da array paralleli di processori HMM sono state implementate su FPGA. Le soluzioni che offrivano un miglior compromesso tra prestazioni e quantità di risorse hardware utilizzate sono state selezionate per ulteriori analisi. Un sistema software per il pattern recognition basato su HMM stato scelto come sistema di riferimento per verificare la corretta funzionalità delle architetture implementate. Diversi test sono stati progettati per validare che il funzionamento del sistema corrispondesse alle specifiche iniziali. Le versioni implementate del sistema sono state confrontate con il software di riferimento sulla base dei risultati forniti dai test. Dal confronto è stato possibile appurare che le architetture sviluppate hanno un comportamento corrispondente a quello richiesto. Infine le implementazioni dell’array parallelo di processori HMM `e sono state applicate a due applicazioni reali: un riconoscitore vocale, ed un classificatore per interfacce basate su segnali elettroencefalografici. In entrambi i casi l’architettura si è dimostrata in grado di gestire l’applicazione senza alcun problema. L’uso del processamento hardware per il riconoscimento vocale apre di fatto la strada a nuovi sviluppi nel campo grazie al notevole incremento di prestazioni ottenibili in termini di tempo di esecuzione. L’applicazione al processamento dell’EEG, invece, introduce di fatto un approccio completamente nuovo alla classificazione di questo tipo di segnali, e mostra come in futuro potrebbe essere possibile lo sviluppo di interfacce basate sulla classificazione dei segnali generati dal pensiero spontaneo. I possibili sviluppi del lavoro iniziato con questa tesi sono molteplici. Una direzione possibile è quella dell’implementazione completa dell’architettura proposta come un sistema stand–alone riconfigurabile per l’accelerazione di sistemi per pattern recognition di qualsiasi natura purchè basati su HMM. Le potenzialità di tale sistema renderebbero possibile la realizzazione di classificatiori in tempo reale con un alto grado di complessità, e quindi allo sviluppo di interfacce realmente multimodali, con una vasta gamma di applicazioni, dai sistemi di per lo spazio a quelli di supporto per persone disabili.In this thesis a new approach is described for the development of human–computer interfaces. In particular the case of pattern recognition systems based on Hidden Markov Models have been taken into account. The research started from he development of techniques for the realization of natural language speech recognition systems. The Hidden Markov Model (HMM) was chosen as the main algorithmic tool to be used to build the system. After the early work the goal was extended to the development of an hardware architecture that provided a reconfigurable tool to be used in any pattern recognition task, and not only in speech recognition. The whole work is thus focused on the development of dedicated hardware architectures, but also some new results have been obtained on the classification of electroencephalographic signals through the use of HMMs. Firstly a system–level architecture has been developed to be used in HMM based pattern recognition systems. The architecture has been conceived in order to be able to work as a stand–alone system. Then a VHDL description has been made of a flexible and completely reconfigurable hardware HMM processor and the design was successfully simulated. A parallel array of these processors is actually the core processing block of the developed architecture. Then two suitable FPGA based, fast prototyping platforms have been identified to be the targets for the implementation tests. Different configurations of parallel HMM processor arrays have been set up and mapped on the target FPGAs. Some solutions have been selected to be the best in terms of balance between performance and resources utilization. Furthermore a software HMM based pattern recognition system has been chosen to be the reference system for the functionality of the implemented subsystems. A set of tests have been developed with the aim to test the correct functionality of the hardware. The implemented system was compared to the reference system on the basis of the tests’ results, and it was found that the behavior was the one expected and the required functionality was correctly achieved. Finally the implementation of the parallel HMM array was tested through its application to two real–world applications: a speech recognition task and a brain–computer interface task. In both cases the architecture showed to be functionally suitable and powerful enough to handle the task without problems. The application of the hardware processing to speech recognition opens new perspectives in the design of this kind of systems because of the dramatic increment in performance. The application to brain–computer interface is really interesting because of a new approach in the classification of EEG that shows how could be possible a future development of interfaces based on the classification of spontaneous thought. The possible evolution directions of the work started with this thesis are many. Effort could be spent of the implementation of the developed architecture as a stand–alone reconfigurable system suitable for any kind of HMM–based pattern recognition task. The potential performance of such a system could open the way to extremely complex real–time pattern recognition systems, and thus to the realization of truly multimodal interfaces, with a variety of applications, from space to aid systems for the impaired

    Personalized Health Monitoring Using Evolvable Block-based Neural Networks

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    This dissertation presents personalized health monitoring using evolvable block-based neural networks. Personalized health monitoring plays an increasingly important role in modern society as the population enjoys longer life. Personalization in health monitoring considers physiological variations brought by temporal, personal or environmental differences, and demands solutions capable to reconfigure and adapt to specific requirements. Block-based neural networks (BbNNs) consist of 2-D arrays of modular basic blocks that can be easily implemented using reconfigurable digital hardware such as field programmable gate arrays (FPGAs) that allow on-line partial reorganization. The modular structure of BbNNs enables easy expansion in size by adding more blocks. A computationally efficient evolutionary algorithm is developed that simultaneously optimizes structure and weights of BbNNs. This evolutionary algorithm increases optimization speed by integrating a local search operator. An adaptive rate update scheme removing manual tuning of operator rates enhances the fitness trend compared to pre-determined fixed rates. A fitness scaling with generalized disruptive pressure reduces the possibility of premature convergence. The BbNN platform promises an evolvable solution that changes structures and parameters for personalized health monitoring. A BbNN evolved with the proposed evolutionary algorithm using the Hermite transform coefficients and a time interval between two neighboring R peaks of ECG signal, provides a patient-specific ECG heartbeat classification system. Experimental results using the MIT-BIH Arrhythmia database demonstrate a potential for significant performance enhancements over other major techniques
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