9 research outputs found
Development of Lifting-based VLSI Architectures for Two-Dimensional Discrete Wavelet Transform
Two-dimensional discrete wavelet transform (2-D DWT) has evolved as an essential
part of a modem compression system. It offers superior compression with good image
quality and overcomes disadvantage of the discrete cosine transform, which suffers
from blocks artifacts that reduces the quality of the inage. The amount of
computations involve in 2-D DWT is enormous and cannot be processed by generalpurpose
processors when real-time processing is required. Th·"efore, high speed and
low power VLSI architecture that computes 2-D DWT effectively is needed. In this
research, several VLSI architectures have been developed that meets real-time
requirements for 2-D DWT applications. This research iaitially started off by
implementing a software simulation program that decorrelates the original image and
reconstructs the original image from the decorrelated image. Then, based on the
information gained from implementing the simulation program, a new approach for
designing lifting-based VLSI architectures for 2-D forward DWT is introduced. As a
result, two high performance VLSI architectures that perform 2-D DWT for 5/3 and
9/7 filters are developed based on overlapped and nonoverlapped scan methods. Then,
the intermediate architecture is developed, which aim a·: reducing the power
consumption of the overlapped areas without using the expensive line buffer. In order
to best meet real-time applications of 2-D DWT with demanding requirements in
terms of speed and throughput parallelism is explored. The single pipelined
intermediate and overlapped architectures are extended to 2-, 3-, and 4-parallel
architectures to achieve speed factors of 2, 3, and 4, respectively. To further
demonstrate the effectiveness of the approach single and para.llel VLSI architectures
for 2-D inverse discrete wavelet transform (2-D IDWT) are developed. Furthermore,
2-D DWT memory architectures, which have been overlooked in the literature, are
also developed. Finally, to show the architectural models developed for 2-D DWT are
simple to control, the control algorithms for 4-parallel architecture based on the first
scan method is developed. To validate architectures develcped in this work five
architectures are implemented and simulated on Altera FPGA.
In compliance with the terms of the Copyright Act 1987 and the IP Policy of the
university, the copyright of this thesis has been reassigned by the author to the legal
entity of the university,
Institute of Technology PETRONAS Sdn bhd.
Due acknowledgement shall always be made of the use of any material contained
in, or derived from, this thesis
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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
On the design of architecture-aware algorithms for emerging applications
This dissertation maps various kernels and applications to a spectrum of programming models and architectures and also presents architecture-aware algorithms for different systems. The kernels and applications discussed in this dissertation have widely varying computational characteristics. For example, we consider both dense numerical computations and sparse graph algorithms. This dissertation also covers emerging applications from image processing, complex network analysis, and computational biology.
We map these problems to diverse multicore processors and manycore accelerators. We also use new programming models (such as Transactional Memory, MapReduce, and Intel TBB) to address the performance and productivity challenges in the problems. Our experiences highlight the importance of mapping applications to appropriate programming models and architectures. We also find several limitations of current system software and architectures and directions to improve those. The discussion focuses on system software and architectural support for nested irregular parallelism, Transactional Memory, and hybrid data transfer mechanisms. We believe that the complexity of parallel programming can be significantly reduced via collaborative efforts among researchers and practitioners from different domains. This dissertation participates in the efforts by providing benchmarks and suggestions to improve system software and architectures.Ph.D.Committee Chair: Bader, David; Committee Member: Hong, Bo; Committee Member: Riley, George; Committee Member: Vuduc, Richard; Committee Member: Wills, Scot
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