Abstract
The designs of application specific integrated circuits and/or multiprocessor systems are usually required in order to improve the performance of multidimensional applications such as digital-image processing and computer vision. Wavelet-based algorithms have been found promising among these applications due to the features of hierarchical signal analysis and multiresolution analysis. Because of the large size of multidimensional input data, off-chip random access memory (RAM) based systems have ever been necessary for calculating algorithms in these applications, where either memory address pointers or data preprocessing and rearrangements in off-chip memories are employed. This paper establishes and follows novel concepts in data dependence analysis for generalized and arbitrarily multidimensional wavelet-based algorithms, i.e., the wavelet-adjacent field and the super wavelet-dependence vector. Based on them, a series of novel nonlinear I/O data space transformations for variable localization and dependence graph regularization for wavelet algorithms is proposed. It leads to general designs of non-RAM-based architectures for wavelet-based algorithms where off-chip communications for intermediate calculation results are eliminated without preprocessing or rearranging input data.
Original language | English (US) |
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Pages (from-to) | 58-74 |
Number of pages | 17 |
Journal | IEEE Transactions on Very Large Scale Integration (VLSI) Systems |
Volume | 13 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2005 |
Keywords
- Dependence graph
- Discrete wavelet transform
- Non-RAM-based architectures
- Zerotree coding
ASJC Scopus subject areas
- Software
- Hardware and Architecture
- Electrical and Electronic Engineering