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

Concurrent error detection in 2-D separable linear transform

As process technology continues to scale to smaller geometries and reduces the supply voltage, reliability of the resulting semiconductor becomes a greater concern. The effect of deep submicron noise, soft errors, variation, and aging degradation pose challenges on the functional correctness of VLSI systems and places roadblocks on reductions in scale. On the other side, as computing moves toward mobile, the energy efficiency of digital systems becomes one of the most important design metrics. However, reliability and energy efficiency are contradicting design requirements. Adding a voltage guard band is the most common method to mitigate the reliability impacts in such instances. Low power design technique like voltage over-scaling (VOS) even reduces the power by scaling the supply voltage just before data-dependant timing errors start to appear. Concurrent error detection is the solution to tackle reliability and energy-efficiency in a unified manner. Fault tolerance can be deployed at different design hierarchies. Given its low overhead, algorithm level error detection is an attractive approach. In this work, a generic weighted checksum code based error detection algorithm targeted generic 2-D separable linear transform is proposed. This technique encodes the input array at the 2-D linear trans- formation level, and algorithms are designed to operate on encoded data and produce encoded output data. The proposed error detection technique is a system-level method and therefore can be used in existing hardware or software 2-D linear transformation architectures with low overhead. The mathematic proof of the algorithm is provided within the scope of this dissertation. The checksum weighting vector for several common transforms are derived as examples, error detection cost and algorithm effectiveness are analyzed. In traditional fault tolerance study, the error is often evaluated at the boolean level. Many DSP applications, like 2-D linear transformation used in the multimedia compression system, do not require exactly correct results, but rather that the quality of the output is within the acceptable range. A generic quality aware error detection in the 2-D separable linear transform is proposed by extending the above property and defining the errors at the functional level. As an example, the quality-aware error detection technique is deployed on a low-power wavelet lifting transform architecture in JPEG2000. A low-cost Signal to Noise Ratio (SNR) aware detection logic based on proposed scheme is integrated into the discrete wavelet lifting transform architecture. This detection logic checks whether the image quality degradation caused by voltage over-scaling induced timing errors is acceptable and determines the optimal voltage set point in operating conditions at run time. This novel quality-based error detection approach is significantly different from traditional error detection schemes which look for exact data equivalence. A simulation result for one design shows that the supply voltage can be scaled down to 75% of the nominal voltage in typical process corner without significant image quality degradation, which translates to 9.15mW power consumption (44% power saving).Electrical and Computer Engineerin

Novel implementation technique for a wavelet-based broadband signal detection system

This thesis reports on the design, simulation and implementation of a novel Implementation for a Wavelet-based Broadband Signal Detection System. There is a strong interest in methods of increasing the resolution of sonar systems for the detection of targets at sea. A novel implementation of a wideband active sonar signal detection system is proposed in this project. In the system the Continuous Wavelet Transform is used for target motion estimation and an Adaptive-Network-based Fuzzy inference System (ANFIS) is adopted to minimize the noise effect on target detection. A local optimum search algorithm is introduced in this project to reduce the computation load of the Continuous Wavelet Transform and make it suitable for practical applications. The proposed system is realized on a Xilinx University Program Virtex-II Pro Development System which contains a Virtex II pro XC2VP30 FPGA chip with 2 powerPC 405 cores. Testing for single target detection and multiple target detection shows the proposed system is able to accurately locate targets under reverberation-limited underwater environment with a Signal-Noise-Ratio of up to -30db, with location error less than 10 meters and velocity estimation error less than 1 knot. In the proposed system the combination of CWT and local optimum search algorithm significantly saves the computation time for CWT and make it more practical to real applications. Also the implementation of ANFIS on the FPGA board indicates in the future a real-time ANFIS operation with VLSI implementation would be possible

High-Speed Pipeline VLSI Architectures for Discrete Wavelet Transforms

The discrete wavelet transform (DWT) has been widely used in many fields, such as image compression, speech analysis and pattern recognition, because of its capability of decomposing a signal at multiple resolution levels. Due to the intensive computations involved with this transform, the design of efficient VLSI architectures for a fast computation of the transforms have become essential, especially for real-time applications and those requiring processing of high-speed data. The objective of this thesis is to develop a scheme for the design of hardware resource-efficient high-speed pipeline architectures for the computation of the DWT. The goal of high speed is achieved by maximizing the operating frequency and minimizing the number of clock cycles required for the DWT computation with little or no overhead on the hardware resources. In this thesis, an attempt is made to reach this goal by enhancing the inter-stage and intra-stage parallelisms through a systematic exploitation of the characteristics inherent in discrete wavelet transforms. In order to enhance the inter-stage parallelism, a study is undertaken for determining the number of pipeline stages required for the DWT computation so as to synchronize their operations and utilize their hardware resources efficiently. This is achieved by optimally distributing the computational load associated with the various resolution levels to an optimum number of stages of the pipeline. This study has determined that employment of two pipeline stages with the first one performing the task of the first resolution level and the second one that of all the other resolution levels of the 1-D DWT computation, and employment of three pipeline stages with the first and second ones performing the tasks of the first and second resolution levels and the third one performing that of the remaining resolution levels of the 2-D DWT computation, are the optimum choices for the development of 1-D and 2-D pipeline architectures, respectively. The enhancement of the intra-stage parallelism is based on two main ideas. The first idea, which stems from the fact that in each consecutive resolution level the input data are decimated by a factor of two along each dimension, is to decompose the filtering operation into subtasks that can be performed in parallel by operating on even- and odd-numbered samples along each dimension of the data. It is shown that each subtask, which is essentially a set of multiply-accumulate operations, can be performed by employing a MAC-cell network consisting of a two-dimensional array of bit-wise adders. The second idea in enhancing the intra-stage parallelism is to maximally extend the bit-wise addition operations of this network horizontally through a suitable arrangement of bit-wise adders so as to minimize the delay of its critical path. In order to validate the proposed scheme, design and implementation of two specific examples of pipeline architectures for the 1-D and 2-D DWT computations are considered. The simulation results show that the pipeline architectures designed using the proposed scheme are able to operate at high clock frequencies, and their performances, in terms of the processing speed and area-time product, are superior to those of the architectures designed based on other schemes and utilizing similar or higher amount of hardware resources. Finally, the two pipeline architectures designed using the proposed scheme are implemented in FPGA. The test results of the FPGA implementations validate the feasibility and effectiveness of the proposed scheme for designing DWT pipeline architectures

PC-grade parallel processing and hardware acceleration for large-scale data analysis

Arguably, modern graphics processing units (GPU) are the first commodity, and desktop parallel processor. Although GPU programming was originated from the interactive rendering in graphical applications such as computer games, researchers in the field of general purpose computation on GPU (GPGPU) are showing that the power, ubiquity and low cost of GPUs makes them an ideal alternative platform for high-performance computing. This has resulted in the extensive exploration in using the GPU to accelerate general-purpose computations in many engineering and mathematical domains outside of graphics. However, limited to the development complexity caused by the graphics-oriented concepts and development tools for GPU-programming, GPGPU has mainly been discussed in the academic domain so far and has not yet fully fulfilled its promises in the real world. This thesis aims at exploiting GPGPU in the practical engineering domain and presented a novel contribution to GPGPU-driven linear time invariant (LTI) systems that are employed by the signal processing techniques in stylus-based or optical-based surface metrology and data processing. The core contributions that have been achieved in this project can be summarized as follow. Firstly, a thorough survey of the state-of-the-art of GPGPU applications and their development approaches has been carried out in this thesis. In addition, the category of parallel architecture pattern that the GPGPU belongs to has been specified, which formed the foundation of the GPGPU programming framework design in the thesis. Following this specification, a GPGPU programming framework is deduced as a general guideline to the various GPGPU programming models that are applied to a large diversity of algorithms in scientific computing and engineering applications. Considering the evolution of GPU’s hardware architecture, the proposed frameworks cover through the transition of graphics-originated concepts for GPGPU programming based on legacy GPUs and the abstraction of stream processing pattern represented by the compute unified device architecture (CUDA) in which GPU is considered as not only a graphics device but a streaming coprocessor of CPU. Secondly, the proposed GPGPU programming framework are applied to the practical engineering applications, namely, the surface metrological data processing and image processing, to generate the programming models that aim to carry out parallel computing for the corresponding algorithms. The acceleration performance of these models are evaluated in terms of the speed-up factor and the data accuracy, which enabled the generation of quantifiable benchmarks for evaluating consumer-grade parallel processors. It shows that the GPGPU applications outperform the CPU solutions by up to 20 times without significant loss of data accuracy and any noticeable increase in source code complexity, which further validates the effectiveness of the proposed GPGPU general programming framework. Thirdly, this thesis devised methods for carrying out result visualization directly on GPU by storing processed data in local GPU memory through making use of GPU’s rendering device features to achieve realtime interactions. The algorithms employed in this thesis included various filtering techniques, discrete wavelet transform, and the fast Fourier Transform which cover the common operations implemented in most LTI systems in spatial and frequency domains. Considering the employed GPUs’ hardware designs, especially the structure of the rendering pipelines, and the characteristics of the algorithms, the series of proposed GPGPU programming models have proven its feasibility, practicality, and robustness in real engineering applications. The developed GPGPU programming framework as well as the programming models are anticipated to be adaptable for future consumer-level computing devices and other computational demanding applications. In addition, it is envisaged that the devised principles and methods in the framework design are likely to have significant benefits outside the sphere of surface metrology.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

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