- author = "Andrew Kinane",
- title = "Energy efficient hardware acceleration of multimedia processing tools",
- school = "School of Electronic Engineering, Dublin City University",
- year = "2006",
- address = "Ireland",
- month = may,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://doras.dcu.ie/17985/",
-
URL = "
http://doras.dcu.ie/17985/1/Andrew_Kinane.pdf",
-
URL = "http://www.eeng.dcu.ie/~kinanea/thesis/abstract.txt",
-
URL = "http://www.eeng.dcu.ie/~kinanea/thesis/kinane_final.pdf",
- size = "263 pages",
-
abstract = "The world of mobile devices is experiencing an ongoing
trend of feature enhancement and general-purpose
multimedia platform convergence. This trend poses many
grand challenges, the most pressing being their limited
battery life as a consequence of delivering
computationally demanding features. The envisaged
mobile application features can be considered to be
accelerated by a set of underpinning hardware blocks.
Based on the survey that this thesis presents on modern
video compression standards and their associated
enabling technologies, it is concluded that tight
energy and throughput constraints can still be
effectively tackled at algorithmic level in order to
design re-usable optimised hardware acceleration
cores.
To prove these conclusions, the work in this thesis is focused on two of the basic enabling technologies that support mobile video applications, namely the Shape Adaptive Discrete Cosine Transform (SA-DCT) and its inverse, the SA-IDCT. The hardware architectures presented in this work have been designed with energy efficiency in mind. This goal is achieved by employing high level techniques such as redundant computation elimination, parallelism and low switching computation structures. Both architectures compare favourably against the relevant prior art in the literature.
The SA-DCT/IDCT technologies are instances of a more general computation -- namely, both are Constant Matrix Multiplication (CMM) operations. Thus, this thesis also proposes an algorithm for the efficient hardware design of any general CMM-based enabling technology. The proposed algorithm leverages the effective solution search capability of genetic programming. A bonus feature of the proposed modelling approach is that it is further amenable to hardware acceleration. Another bonus feature is an early exit mechanism that achieves large search space reductions. Results show an improvement on state of the art algorithms with future potential for even greater savings.",
-
notes = "Supervisor: Dr. Noel E. O'Connor.
ID Code: 17985",
