Low power two-channel PR QMF bank using CSD coefficients and FPGA implementation

Finite impulse response (FIR) filter is a fundamental component in digital signal processing. Two-channel perfect reconstruction (PR) QMF banks are widely used in many applications, such as image coding, speech processing and communications. A practical lattice realization of two-channel QMF bank is developed in this thesis for dealing with the wide dynamic range of intermediate results in lattice structure. To achieve low complexity and low power consumption of two-channel perfect reconstruction QMF bank, canonical signed digit (CSD) number system is used for representing lattice coefficients in FPGA implementation. Utilization of CSD number system in lattice structures leads to more efficient hardware implementation. Many fixed-point simulations were done in Matlab in order to obtain the proper fixed-point word-length for different signals. Finally, FPGA implementation results show that perfect reconstruction signal is obtained by using the proposed method. Furthermore, the power consumption using CSD number system for representing lattice coefficients is less than that obtained by using two\u27s complement number system in two-channel QMF bank. A low complexity and low power two-channel PR QMF bank using CSD coefficients was realized

On the Hardware/Software Design and Implementation of a High Definition Multiview Video Surveillance System

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Continuous Integration for Fast SoC Algorithm Development

Digital systems have become advanced, hard to design and optimize due to ever-growing technology. Integrated Circuits (ICs) have become more complicated due to complex computations in latest technologies. Communication systems such as mobile networks have evolved and become a part of our daily lives with the advancement in technology over the years. Hence, need of efficient, reusable and automated processes for System-on-a-Chip (SoC) development has been increased. Purpose of this thesis is to study and evaluate currently used SoC development processes and presents guidelines on how these processes can be streamlined. The thesis starts by evaluating currently used SoC development flows and their advantages and disadvantages. One important aspect is to identify step which cause duplication of work and unnecessary idle times in SoC development teams. A study is conducted and input from SoC development experts is taken in order to optimize SoC flows and use of Continuous Integration (CI) system. An algorithm model is implemented that can be used in multiple stages of SoC development at adequate complexity and is “easy enough” to be used for a person not mastering the topic. The thesis outcome is proposal for CI system in SoC development for accelerating the speed and reliability of implementing algorithms to RTL code and finally into product. CI system tool is also implemented to automate and test the model design so that it also remains up to date

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

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.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

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

RAPID CLOCK RECOVERY ALGORITHMS FOR DIGITAL MAGNETIC RECORDING AND DATA COMMUNICATIONS

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN024293 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Embedded Machine Learning: Emphasis on Hardware Accelerators and Approximate Computing for Tactile Data Processing

Machine Learning (ML) a subset of Artificial Intelligence (AI) is driving the industrial and technological revolution of the present and future. We envision a world with smart devices that are able to mimic human behavior (sense, process, and act) and perform tasks that at one time we thought could only be carried out by humans. The vision is to achieve such a level of intelligence with affordable, power-efficient, and fast hardware platforms. However, embedding machine learning algorithms in many application domains such as the internet of things (IoT), prostheses, robotics, and wearable devices is an ongoing challenge. A challenge that is controlled by the computational complexity of ML algorithms, the performance/availability of hardware platforms, and the application\u2019s budget (power constraint, real-time operation, etc.). In this dissertation, we focus on the design and implementation of efficient ML algorithms to handle the aforementioned challenges. First, we apply Approximate Computing Techniques (ACTs) to reduce the computational complexity of ML algorithms. Then, we design custom Hardware Accelerators to improve the performance of the implementation within a specified budget. Finally, a tactile data processing application is adopted for the validation of the proposed exact and approximate embedded machine learning accelerators. The dissertation starts with the introduction of the various ML algorithms used for tactile data processing. These algorithms are assessed in terms of their computational complexity and the available hardware platforms which could be used for implementation. Afterward, a survey on the existing approximate computing techniques and hardware accelerators design methodologies is presented. Based on the findings of the survey, an approach for applying algorithmic-level ACTs on machine learning algorithms is provided. Then three novel hardware accelerators are proposed: (1) k-Nearest Neighbor (kNN) based on a selection-based sorter, (2) Tensorial Support Vector Machine (TSVM) based on Shallow Neural Networks, and (3) Hybrid Precision Binary Convolution Neural Network (BCNN). The three accelerators offer a real-time classification with monumental reductions in the hardware resources and power consumption compared to existing implementations targeting the same tactile data processing application on FPGA. Moreover, the approximate accelerators maintain a high classification accuracy with a loss of at most 5%