Signal processing computational needs
Abstract
Previous reviews of signal processing computational needs and their systolic implementation have emphasized the need for a small set of matrix operations, primarily matrix multiplication, orthogonal triangularization, triangular backsolve, singular value decomposition, and the generalized singular value decomposition. Algorithms and architectures for these tasks are sufficiently well understood to begin transitioning from search to exploratory development. Substantial progress has also been reported on parallel algorithms for updating symmetric eigensystems and the singular value decomposition. Another problem which has proved to be easier than expected is inner product computation for highspeed high resolution predictive analogtodigital conversion. Although inner product computation in a general setting will require O(log n) time via a tree, the special structure of the prediction permits the use of a systolic transversal filter, producing a new predicted value in time O(1). Problem areas which are still in an early stage of study include parallel algorithms for the WignerVille Distribution function, L1 norm approximation, inequality constrained least squares, and the total least squares problem.
 Publication:

Professional Paper
 Pub Date:
 November 1987
 Bibcode:
 1987nosc.reptR....S
 Keywords:

 Architecture (Computers);
 Least Squares Method;
 Parallel Processing (Computers);
 Signal Processing;
 Algorithms;
 Decomposition;
 Matrices (Mathematics);
 Communications and Radar