Optimal Scheduling of Digital Signal Processing Data-flow Graphs using Shortest-path Algorithms

2002 ◽  
Vol 45 (1) ◽  
pp. 88-100 ◽  
Author(s):  
A. Shatnawi
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Shortest Path ◽  
Data Flow ◽  
Digital Signal ◽  
Optimal Scheduling ◽  
Processing Data ◽  
Data Flow Graphs ◽  
Shortest Path Algorithms ◽  
Flow Graphs

Signal Processing

2016 ◽  
pp. 153-177
Author(s):  
Deepika Ghai ◽  
Neelu Jain
Keyword(s):  
Signal Processing ◽  
Data Flow ◽  
Digital Signal ◽  
Rate Data ◽  
Data Flow Graph ◽  
Flow Graph ◽  
Iteration Bound ◽  
Signal Processing Algorithms ◽  
Data Flow Graphs ◽  
Flow Graphs

Digital signal processing algorithms are recursive in nature. These algorithms are explained by iterative data-flow graphs where nodes represent computations and edges represent communications. For all data-flow graphs, time taken to achieve output from the applied input is referred as iteration bound. In this chapter, two algorithms are used for computing the iteration bound i.e. Longest Path Matrix (LPM) and Minimum Cycle Mean (MCM). The iteration bound of single-rate data-flow graph (SRDFG) can be determined by considering the Multi-rate data-flow graph (MRDFG) equivalent of the SRDFG. SRDFG contain more nodes and edges as compared to MRDFG. Reduction of nodes and edges in MRDFG is used for faster determination of the iteration bound.

Static Optimal Scheduling for Synchronous Data Flow Graphs with Model Checking

2015 ◽  
pp. 551-569 ◽  
Author(s):  
Xue-Yang Zhu ◽  
Rongjie Yan ◽  
Yu-Lei Gu ◽  
Jian Zhang ◽  
Wenhui Zhang ◽  
...  
Keyword(s):  
Model Checking ◽  
Data Flow ◽  
Optimal Scheduling ◽  
Data Flow Graphs ◽  
Synchronous Data Flow ◽  
Flow Graphs

Compact Form of Mason's Gain Formula for Signal Flow Graphs

2010 ◽  
Vol 47 (4) ◽  
pp. 393-403 ◽  
Author(s):  
V. C. Prasad
Keyword(s):  
Signal Processing ◽  
Graph Theory ◽  
Digital Signal Processing ◽  
Control Systems ◽  
Digital Signal ◽  
Tree Structure ◽  
New Techniques ◽  
Signal Flow ◽  
Signal Flow Graphs ◽  
Flow Graphs

Mason's gain formula requires combining all paths and all loops judiciously. New techniques are presented in this paper to do this. All possible non-touching loop combinations can be generated systematically and represented compactly using a tree structure and/or factoring technique. Both numerator and denominator of the formula can be treated identically. The approach is simple enough to be used in teaching students Mason's gain formula as part of courses in control systems, digital signal processing, graph theory and applications, among others.

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