GA Evolved Configuration Data for Embryonic Architecture with Built-in Self-test

Abstract

The embryonic architecture, which draws inspiration from the biological process of ontogeny, has built-in capabilities for self-repair. The embryonic cells have a complete genome, allowing the data to be replicated to a cell that is not defective in the event of a cell failure in the embryonic fabric. A novel, specially designed genetic algorithm (GA) is used to evolve the configuration information for embryonic cells. Any failed embryonic cell must be notified by the proposed Built-in Self-test (BIST) module of the embryonic fabric. In this study, an effective centralized BIST design for a novel embryonic fabric is suggested. If the self-test mode is activated, the proposed BIST scans every embryonic cell. To optimize the data size, the genome or configuration data of each embryonic cell is decoded using the Cartesian Genetic Programming (CGP) format. This study evaluates the GA’s performance on 1-bit adder and 2-bit comparator circuits present in embryonic cells. Fault detection at every cell is made possible by the BIST module’s design. Additionally, the CGP format can offer gate-level fault detection. Customized GA and BIST are coupled with the novel embryonic architecture. The embryonic cell can perform self-repair through the process of scrubbing data for temporary defects.