author = {Strzodka, Robert},
  title = {Hardware Efficient PDE Solvers in Quantized Image Processing},
  school = {University Duisburg},
  year = {2004},
  type = {Dissertation},
  abstract = {Performance and accuracy of scientific computations are competing
	aspects. A close interplay between the design of computational schemes
	and their implementation can improve both aspects by making better
	use of the available resources. The thesis describes the design of
	robust schemes under strong quantization and their hardware efficient
	implementation on data-stream-based architectures for PDE based image
	processing. The strong quantization improves execution time, but
	renders traditional error estimates useless. The precision of the
	number formats is too small to control the quantitative error in
	iterative schemes. Instead, quantized schemes which preserve the
	qualitative behavior of the continuous models are constructed. In
	particular for the solution of the quantized anisotropic diffusion
	model one can derive a quantized scale-space with almost identical
	properties to the continuous one. Thus the image evolution is accurately
	reconstructed despite the inability to control the error in the long
	run, which is difficult even for high precision computations. All
	memory intensive algorithms are, nowadays, burdened with the memory
	gap problem which degrades performance enormously. The instruction-stream-based
	computing paradigm reenforces this problem, whereas architectures
	subscribing to data-stream-based computing offer more possibilities
	to bridge the gap between memory and logic performance. Also more
	parallelism is available in these devices. Three architectures of
	this type are covered: graphics hardware, reconfigurable logic and
	reconfigurable computing devices. They allow to exploit the parallelism
	inherent in image processing applications and apply a memory efficient
	usage. Their pros and cons and future development are discussed.
	The combination of robust quantized schemes and hardware efficient
	implementations deliver an accurate reproduction of the continuous
	evolution and significant performance gains over standard software
	solutions. The applied devices are available on affordable AGP/PCI
	boards, offering true alternatives even to small multi-processor
  pdf = { 1}