Concurrent algorithms and pipelined processors via decomposition and data-flow principle for recursive filters
The majority of signal-processing algorithms involve complex numerical matrix algebra, and this requires intensive real-time computing speed. To achieve the speed-up for real-time computations, a parallel-processing environment should be designed. A proper combination of the algorithmic and architectural parallelism is necessary for this processing environment. This thesis deals with the design of a concurrent algorithm and a pipelined architecture for recursive filters. The data-level decomposition method is used to obtain a parallel algorithm for the Kalman filter. This method is based on the multiple-data-processing strategy in which the data are segmented, and the segments are processed in parallel. A new approach for the data-level decomposition of the Kalman filter based on the oblique projection is presented. A new pipelined architecture for the VLSI implementation of recursive filters is developed to achieve higher speed-up. The new architecture, termed independent-data-flow-wavefront-array processors (IDFWAP), is a data-flow machine with a feed-back structure suitable for the recursive filters.
- Research Organization:
- Washington State Univ., Pullman (USA)
- OSTI ID:
- 6355123
- Resource Relation:
- Other Information: Thesis (Ph. D.)
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
PARALLEL PROCESSING
COMPUTER ARCHITECTURE
SIGNAL CONDITIONING
REAL TIME SYSTEMS
VECTOR PROCESSING
ALGORITHMS
DATA PROCESSING
DIGITAL FILTERS
INTEGRATED CIRCUITS
ELECTRONIC CIRCUITS
MATHEMATICAL LOGIC
MICROELECTRONIC CIRCUITS
PROCESSING
PROGRAMMING
990210* - Supercomputers- (1987-1989)