Performance Analysis of Modified Lifting Based DWT Architecture and FPGA Implementation for Speed and Power

Demand for high speed and low power architecture for DWT computation have led to design of novel algorithms and architecture In this paper we design model and implement a hardware efficient high speed and power efficient DWT architecture based on modified lifting scheme algorithm The design is interfaced with SIPO and PISO to reduce the number of I O lines on the FPGA The design is implemented on Spartan III device and is compared with lifting scheme logic The proposed design operates at frequency of 280 MHz and consumes power less than 42 mW The presynthesis and post-synthesis results are verified and suitable test vectors are used in verifying the functionality of the design The design is suitable for real time data processin

Zooplankton visualization system: design and real-time lossless image compression

In this thesis, I present a design of a small, self-contained, underwater plankton imaging system. I base the imaging system’s design on an embedded PC architecture based on PC/104-Plus standards to meet the compact size and low power requirements. I developed a simple graphical user interface to run on a real-time operating system to control the imaging system. I also address how a real-time image compression scheme implemented on an FPGA chip speeds up image transfer speeds of the imaging system. Since lossless compression of the image is required in order to retain all image details, I began with an established compression scheme like SPIHT, and latter proposed a new compression scheme that suits the imaging system’s requirements. I provide an estimate of the total amount of resources required and propose suitable FPGA chips to implement the compression scheme. Finally, I present various parallel designs by which the FPGA chip can be integrated into the imaging system

Hyper-Spectral Image Processing Using High Performance Reconfigurable Computers

The purpose of this thesis is to investigate the methods of implementing a section of a Matlab hyper-spectral image processing software application into a digital system that operates on a High Performance Reconfigurable Computer. The work presented is concerned with the architecture, the design techniques, and the models of digital systems that are necessary to achieve the best overall performance on HPRC platforms. The application is an image-processing tool that detects the tumors in a chicken using analysis of a hyper-spectral image. Analysis of the original Matlab code has shown that it gives low performance in achieving the result. The implementation is performed using a three-stage approach. In the first stage, the Matlab code is converted into C++ code in order to identify the bottlenecks that require the most resources. During the second stage, the digital system is designed to optimize the performance on a single reconfigurable computer. In the final stage of the implementation, this work explores the HPRC architecture by deploying and testing the digital design on multiple machines. The research shows that HPRC platforms grant a noticeable performance boost. Furthermore, the more hyper-spectral bands exist in the input image data, the better of the speedup can be expected from the HPRC design work

Efficient architectures of heterogeneous fpga-gpu for 3-d medical image compression

The advent of development in three-dimensional (3-D) imaging modalities have generated a massive amount of volumetric data in 3-D images such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US). Existing survey reveals the presence of a huge gap for further research in exploiting reconfigurable computing for 3-D medical image compression. This research proposes an FPGA based co-processing solution to accelerate the mentioned medical imaging system. The HWT block implemented on the sbRIO-9632 FPGA board is Spartan 3 (XC3S2000) chip prototyping board. Analysis and performance evaluation of the 3-D images were been conducted. Furthermore, a novel architecture of context-based adaptive binary arithmetic coder (CABAC) is the advanced entropy coding tool employed by main and higher profiles of H.264/AVC. This research focuses on GPU implementation of CABAC and comparative study of discrete wavelet transform (DWT) and without DWT for 3-D medical image compression systems. Implementation results on MRI and CT images, showing GPU significantly outperforming single-threaded CPU implementation. Overall, CT and MRI modalities with DWT outperform in term of compression ratio, peak signal to noise ratio (PSNR) and latency compared with images without DWT process. For heterogeneous computing, MRI images with various sizes and format, such as JPEG and DICOM was implemented. Evaluation results are shown for each memory iteration, transfer sizes from GPU to CPU consuming more bandwidth or throughput. For size 786, 486 bytes JPEG format, both directions consumed bandwidth tend to balance. Bandwidth is relative to the transfer size, the larger sizing will take more latency and throughput. Next, OpenCL implementation for concurrent task via dedicated FPGA. Finding from implementation reveals, OpenCL on batch procession mode with AOC techniques offers substantial results where the amount of logic, area, register and memory increased proportionally to the number of batch. It is because of the kernel will copy the kernel block refer to batch number. Therefore memory bank increased periodically related to kernel block. It was found through comparative study that the tree balance and unroll loop architecture provides better achievement, in term of local memory, latency and throughput

Efficient reconfigurable architectures for 3D medical image compression

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Recently, the more widespread use of three-dimensional (3-D) imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US) have generated a massive amount of volumetric data. These have provided an impetus to the development of other applications, in particular telemedicine and teleradiology. In these fields, medical image compression is important since both efficient storage and transmission of data through high-bandwidth digital communication lines are of crucial importance. Despite their advantages, most 3-D medical imaging algorithms are computationally intensive with matrix transformation as the most fundamental operation involved in the transform-based methods. Therefore, there is a real need for high-performance systems, whilst keeping architectures exible to allow for quick upgradeability with real-time applications. Moreover, in order to obtain efficient solutions for large medical volumes data, an efficient implementation of these operations is of significant importance. Reconfigurable hardware, in the form of field programmable gate arrays (FPGAs) has been proposed as viable system building block in the construction of high-performance systems at an economical price. Consequently, FPGAs seem an ideal candidate to harness and exploit their inherent advantages such as massive parallelism capabilities, multimillion gate counts, and special low-power packages. The key achievements of the work presented in this thesis are summarised as follows. Two architectures for 3-D Haar wavelet transform (HWT) have been proposed based on transpose-based computation and partial reconfiguration suitable for 3-D medical imaging applications. These applications require continuous hardware servicing, and as a result dynamic partial reconfiguration (DPR) has been introduced. Comparative study for both non-partial and partial reconfiguration implementation has shown that DPR offers many advantages and leads to a compelling solution for implementing computationally intensive applications such as 3-D medical image compression. Using DPR, several large systems are mapped to small hardware resources, and the area, power consumption as well as maximum frequency are optimised and improved. Moreover, an FPGA-based architecture of the finite Radon transform (FRAT)with three design strategies has been proposed: direct implementation of pseudo-code with a sequential or pipelined description, and block random access memory (BRAM)- based method. An analysis with various medical imaging modalities has been carried out. Results obtained for image de-noising implementation using FRAT exhibits promising results in reducing Gaussian white noise in medical images. In terms of hardware implementation, promising trade-offs on maximum frequency, throughput and area are also achieved. Furthermore, a novel hardware implementation of 3-D medical image compression system with context-based adaptive variable length coding (CAVLC) has been proposed. An evaluation of the 3-D integer transform (IT) and the discrete wavelet transform (DWT) with lifting scheme (LS) for transform blocks reveal that 3-D IT demonstrates better computational complexity than the 3-D DWT, whilst the 3-D DWT with LS exhibits a lossless compression that is significantly useful for medical image compression. Additionally, an architecture of CAVLC that is capable of compressing high-definition (HD) images in real-time without any buffer between the quantiser and the entropy coder is proposed. Through a judicious parallelisation, promising results have been obtained with limited resources. In summary, this research is tackling the issues of massive 3-D medical volumes data that requires compression as well as hardware implementation to accelerate the slowest operations in the system. Results obtained also reveal a significant achievement in terms of the architecture efficiency and applications performance.Ministry of Higher Education Malaysia (MOHE), Universiti Tun Hussein Onn Malaysia (UTHM) and the British Counci

Towards Complete Emulation of Quantum Algorithms using High-Performance Reconfigurable Computing

Quantum computing is a promising technology that can potentially demonstrate supremacy over classical computing in solving specific classically-intractable problems. However, in its current nascent stage, quantum computing faces major challenges. Two of the main challenges are quantum state decoherence and low scalability of current quantum devices. Decoherence is a process in which the state of the quantum computer is destroyed by interaction with the environment. Decoherence places constraints on the realistic applicability of quantum algorithms as real-life applications usually require complex equivalent quantum circuits to be realized. For example, encoding classical data on quantum computers for solving I/O and data-intensive applications generally requires complex quantum circuits that violate decoherence constraints. In addition, current quantum devices are of intermediate scale, having low quantum bit (qubit) counts and often producing inaccurate or noisy measurements. Consequently, benchmarking of existing quantum algorithms and the investigation of new applications are heavily dependent on classical simulations that use costly, resource-intensive computing platforms. Hardware-based emulation has been alternatively proposed as a more cost-effective and power-efficient approach. Hardware-based emulation methods can take advantage of hardware parallelism and acceleration to produce results at a higher throughput and lower power requirements.This work proposes a hardware-based emulation methodology for quantum algorithms, using cost-effective Field Programmable Gate Array (FPGA) technology. The proposed methodology consists of three components that are required for complete emulation of quantum algorithms; the first component models classical-to-quantum (C2Q) data encoding, the second emulates the behavior of quantum algorithms, and the third models the process of measuring the quantum state and extracting classical information, i.e., quantum-to-classical (Q2C) data decoding. The proposed emulation methodology is used to investigate and optimize methods for C2Q/Q2C data encoding/decoding, as well as several important quantum algorithms such as Quantum Fourier Transform (QFT), Quantum Haar Transform (QHT), and Quantum Grover’s Search (QGS). This work delivers contributions in terms of reducing complexities of quantum circuits, extending and optimizing quantum algorithms, and developing new quantum applications. For example, decoherence-optimized circuits for C2Q/Q2C data encoding/decoding are proposed and evaluated using the proposed emulation methodology. Multi-level decomposable forms of optimized QHT circuits are presented and used to demonstrate dimension reduction of high-resolution data. Additionally, a novel extension to the QGS algorithm is proposed to enable search for dynamically changing multi-patterns of unordered data. Finally, a novel quantum application is presented that combines QHT and dynamic multi-pattern QGS to perform pattern recognition using dimension reduction on high-resolution spatio-spectral data. For higher emulation performance and scalability of the framework, hardware design techniques and hardware architectural optimizations are investigated and proposed. The emulation architectures are designed and implemented on a high-performance reconfigurable computer (HPRC). For reference and comparison, implementations of the proposed quantum circuits are also performed on a state-of-the-art quantum computer. Experimental results show that the proposed hardware architectures enable emulation of quantum algorithms with higher scalability, higher accuracy, and higher throughput, compared to existing hardware-based emulators. As a case study, quantum image processing using multi-spectral images is considered for the experimental evaluations. The analysis and results of this work demonstrate that quantum computers and methodologies based on quantum algorithms will be highly useful in realistic data-intensive domains such as remote-sensing hyperspectral imagery and high-energy physics (HEP)

FlexWAFE - eine Architektur für rekonfigurierbare-Bildverarbeitungssysteme

Recently there has been an increase in demand for high-resolution digital media content in both cinema and television industries. Currently existing equipment does not meet the requirements, or is too costly. New hardware systems and new programming techniques are needed in order to meet the high-resolution, high-quality, image requirements and reduce costs. The industry seeks a flexible architecture capable of running multiple applications on top of standard off-the-shelf components, with reduced development time. Until now, standard practice has been to develop specialized architectures and systems that target a single application. This has little flexibility and leads to high developments costs, every new application is designed almost from scratch. Our focus was to develop an architecture that is suited to image stream processing and has the flexibility to run multiple applications using the same FPGA-based hardware platform. The novelty in our approach is that we reconfigure parts of the architecture at run-time, but without incurring in the time and added constraints penalty of FPGA-partial-reconfiguration techniques. The architecture uses a hierarchical control structure that is well suited to parallel processing, and allows single cycle latency reconfiguration of parts of the processing pipeline. This is achieved using relatively little resources for the distributed control structures. To test the developed architecture a complex film-grain noise reduction algorithm was implemented on an off-the-shelf hardware platform developed by Thomson-Grass Valley. The system meet all the requirements and had very little load on the hierarchical control structures, there is growth headroom for much complexer control demands. The architecture has been ported to other hardware platforms, and other applications have been implemented as well. The run-time reconfigurability has proven to be a key factor in the success of the FlexWAFE.Kürzlich gab es eine Zunahme der Nachfrage nach hochauflösenden digitalen Medieninhalten in den Kino- und Fernsehenindustrien. Derzeit vorhandene Systeme entsprechen nicht den Anforderungen, oder sind zu teuer. Neue Hardware-Systeme und neuer Programmiertechniken sind erforderlich, um den hochauflösenden, hochwertigen, Bildanforderungen zu genügen und Kosten zu verringern. Die Industrie sucht eine flexible Architektur zur Ausführung mehrerer Anwendungen auf Standard-Komponenten, mit reduzierten Entwicklungszeiten. Bis jetzt ist gängige Praxis, spezialisierten Architektur und Systeme zu entwickeln, die eine einzelne Anwendung zielen. Dieses hat wenig Flexibilität und führt zu hohe Entwicklungskosten, jede neue Anwendung ist fast von Grund auf neu konzipiert. Unser Fokus war es, eine für Bild Verarbeitung geeignet Architektur zu entwickeln dass die Flexibilität hat mehrere Anwendungen an dieselbe FPGA-basierte Hardware-Plattform zu laufen. Die Neuheit in unserem Ansatz ist, dass wir Teile der Architektur zur Laufzeit rekonfigurieren, aber, ohne das Zeit und constraints strafe von FPGA Partielle-Rekonfiguration-Techniken. Die Architektur verwendet eine hierarchische Kontrollstruktur, die zur parallel Verarbeitung gut geeignet ist, und Single-Cycle-Latenz Rekonfiguration von Teilen der Verarbeitungs-Pipeline ermöglicht. Dieses wird unter Verwendung relativ weniger Ressourcen für die verteiltes Steuerung Strukturen erzielt. Um das entwickelte Architektur zu testen ein komplexer Film-Korn-Rauschunterdrückung Algorithmus wurde auf einer von Thomson-Grass Valley entwickelt standard Hardware-Plattform umgesetzt. Das System erfüllt alle Anforderungen und hatte sehr wenig Last auf den hierarchischen Kontrollstrukturen, es gibt viel Wachstum Spielraum für viel kompliziertere Steuerunganforderungen. Die Architektur ist zu anderen Hardwareplattformen portiert worden, und andere Anwendungen wurden ebenfalls implementiert. Der Laufzeitreconfigurability ist ein Schlüsselfaktor im Erfolg des FlexWAFE gewesen

Image Processing Using FPGAs

This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs