Sustainable Modular Adaptive Redundancy Technique Emphasizing Partial Reconfiguration for Reduced Power Consumption

Table 10

Comparison of the RARS prototype and three other edge-detection evolution techniques.

	Hollingworth et al. [41]	Gudmundsson et al. [39]	Ross et al. [42]	SMART
Application	Generic images (fairly simple)	Unfragmented, localized thin edges in medical images.	Microscopic images from mineral samples.	Generic (satellite images, uniform patterns, etc.).
Methodology	Exploit inherent parallelism in images	Split image into linked subimages. Maintain links between adjacent pixels.	Implement a training stage (requires sampling 23.6% of image), followed by genetic programming.	Evolve a subset of the edge detector (i.e., critical LUTs) to recover from faults.
Fitness evaluation	Software model	Software model	Software model	Intrinsic evolution
Evolutionary algorithm	Genetic programming.	2D genetic algorithm with problem-specific operators.	Genetic programming training (~25%) and evolution (~75%).	Genetic algorithm.
Genetic String coding	Four node functions (i.e., and, or, not, and xor) and eight terminal values for pixels around the evolved pixel.	Edge map. Image pixels are masked with corresponding values in pixel map (i.e., 0: no edge, 1: edge).	High-level functions (i.e., avg, min, max, and stdev). Terminal pixels and high-level ephemerals (i.e., gradient and intensity).	Direct bitstream evolution. The solution coding is the actual bitfile.
Fitness function	Pratt figure of merit (PFM) relative to fault-free Sobel edge detector	Highly complex cost function based on five cost factors.	Biased random sampling fitness evaluation for training. Program fitness is similar to PFM.	Model-free, triplex discrepancy-based function. No application-specific a priori knowledge needed.
Evolution speed	Partial solution in 2,333 generations after 24 hours of evolution time.	2,300 generations used for ring imaging; 300 generations used for thin, well-localized edges.	75 generations, with 25% of images used for training. Very large population size of 2,000.	148 generations, with low population size of 10. Evolved 8 critical LUTs.
Best fitness	Not reported	0.85 PFM with scaling factor of 0.01.	0.590 for Image 1;0.633 for Image 2.	100% as compared to output from fault-free Sobel edge detector.