@ARTICLE{Malhotra_Gayatri_Embryonic_2023,
 author={Malhotra, Gayatri and Duraiswamy, Punithavathi and Kishore, J.K.},
 volume={vol. 69},
 number={No 2},
 journal={International Journal of Electronics and Telecommunications},
 pages={211-217},
 howpublished={online},
 year={2023},
 publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
 abstract={The embryonic architecture, which draws inspiration from the biological process of ontogeny, has built-in mechanisms for self-repair. The entire genome is stored in the embryonic cells, allowing the data to be replicated in healthy cells in the event of a single cell failure in the embryonic fabric. A specially designed genetic algorithm (GA) is used to evolve the configuration information for embryonic cells. Any failed embryonic cell must be indicated via the proposed Built-in Selftest (BIST) the module of the embryonic fabric. This paper recommends an effective centralized BIST design for a novel embryonic fabric. Every embryonic cell is scanned by the proposed BIST in case the self-test mode is activated. The centralized BIST design uses less hardware than if it were integrated into each embryonic cell. To reduce the size of the data, the genome or configuration data of each embryonic cell is decoded using Cartesian Genetic Programming (CGP). The GA is tested for the 1-bit adder and 2-bit comparator circuits that are implemented in the embryonic cell. Fault detection is possible at every function of the cell due to the BIST module’s design. The CGP format can also offer gate-level fault detection. Customized GA and BIST are combined with the novel embryonic architecture. In the embryonic cell, self-repair is accomplished via data scrubbing for transient errors.},
 type={Article},
 title={Embryonic Architecture with Built-in Self-test and GA Evolved Configuration Data},
 URL={http://journals.pan.pl/Content/127348/PDF/1_3944_Malhotra_sk1.pdf},
 doi={10.24425/ijet.2023.144352},
 keywords={Embryonic, BIST, Self-test, Genetic Algorithm, Cartesian Genetic Programming},
}