Non-RAM-based architectural designs of wavelet-based digital systems based on novel nonlinear I/O data space transformations

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.