Embryonic Architecture with Built-in Self-test and GA Evolved Configuration Data
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 Self-test
(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.
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X. Zhang, G. Dragffy, A. G. Pipe, N. Gunton and Q. M. Zhu, ”A
Reconfigurable Self-healing Embryonic Cell Architecture”, in Proc. of
the International Conference on Engineering of Reconfigurable Systems and Algorithms, 2003, pp. 134–140.
G. Martinovi´c and I. Novak, ”A combined architecture of biologically inspired approaches to self-healing in embedded systems”, in Proc. of International Conference on Smart Systems and Technologies, 2017, pp. 17–22, Paper identifier (https://doi.org/10.1109/SST.2017.8188663).
Y. Shanshan, W. Youren, ”A new self-repairing digital circuit based on embryonic cellular array”, 8th International Conference on Solid-State and Integrated Circuit Technology, 2006, pp. 1997–1999, Paper identifier
(https://doi.org/10.1109/ICSICT.2006.306573).
Z. Zhang, Y. Wang, ”Method to self-repairing reconfiguration strategy selection of embryonic cellular array on reliability analysis”,
in Proc. of the 2014 NASA/ESA Conference on Adaptive
Hardware and Systems, 2014, pp. 225–232, Paper identifier
(https://doi.org/10.1109/AHS.2014.6880181).
Z. Zhai, Q. Yao, Y. Xiaoliang, Y. Rui and W. Youren, ”Self-healing
strategy for transient fault cell reutilization of embryonic array circuit”,
NASA/ESA Conference on Adaptive Hardware and Systems, 2018, pp.
–232, Paper identifier (https://doi.org/10.1109/AHS.2018.8541472).
R. Salvador, A. Otero, J. Mora, E. D. La Torre, L. Sekanina and
T. Riesgo, ”Fault tolerance analysis and self-healing strategy of autonomous, evolvable hardware systems”, International Conference on Reconfigurable Computing and FPGAs, 2011, pp. 164–169, Paper
identifier (https://doi.org/10.1109/ReConFig.2011.37).
E. Benkhelifa, A. Pipe and A. Tiwari, ”Evolvable embryonics: 2-in-
approach to self-healing systems”, Procedia CIRP, 11, 2013, pp.
–399, Paper identifier (https://doi.org/10.1016/j.procir.2013.07.029).
V. Sahni and V. P. Pyara, ”An Embryonic Approach to Reliable Digital Instrumentation Based on Evolvable Hardware”, IEEE Transactions on Instrumentation and Measurement, 52(6), 2003, pp. 1696–1702, Paper
identifier (https://doi.org/10.1109/TIM.2003.818737).
K.H. Chong, I.B. Aris, M.A. Sinan and B.M. Hamiruce, ”Digital
Circuit Structure Design via Evolutionary Algorithm Method”, Journal
of Applied Sciences, 7, 2007, pp. 380-385.
E. Benkhelifa, A. Pipe, G. Dragffy and M. Nibouche, ”Towards evolving fault tolerant biologically inspired hardware using evolutionary algorithms”, IEEE Congress on Evolutionary Computation, Singapore, 2007, pp. 1548-1554, Paper identifier (doi: 10.1109/CEC.2007.4424657).
J. F. Miller, ”Cartesian Genetic Programming. Natural Computing
Series”, 43, 2011, Paper identifier (https://doi.org/10.1007/978-3-642- 17310-3).
G. Malhotra, V. Lekshmi, S. Sudhakar and S. Udupa, ”Implementation of threshold comparator using Cartesian genetic programming on embryonic fabric”, Advances in Intelligent Systems and Computing, 939, 2019, pp. 93–102.
E. Stomeo, T. Kalganova and C. Lambert, ”A novel genetic
algorithm for evolvable hardware”, IEEE Congress on
Evolutionary Computation, 2006, pp. 134–141, Paper identifier
(https://doi.org/10.1109/CEC.2006.1688300).
Lucian Prodan, Gianluca Tempesti, Daniel Mange and Andr´e Stauffer,
”Embryonics: electronic stem cells”, In Proc. of the eighth international conference on Artificial life, 2003, pp. 101–105.
D. Mange, A. Stauffer and G. Tempesti, ”Embryonics: A macroscopic view of the cellular architecture”, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1478, 1998, pp. 174–184, Paper identifier
(https://doi.org/10.1007/BFb0057619).
Yann Thoma, Gianluca Tempesti and Eduardo Sanchez, ”POEtic: An Electronic Tissue for Bio-Inspired Cellular Applications”, Biosystems, vol. 76, 1-3 (2004).
A. Stauffer, Daniel Mange, and Joel Rossier, ”Design of Self-organizing Bio-inspired Systems”, Second NASA/ESA Conference on Adaptive Hardware and Systems, 2007.
M. R. Boesen and J. Madsen, ”eDNA: A bio-inspired reconfigurable hardware cell architecture supporting self-organisation and self-healing”, NASA/ESA Conference on Adaptive Hardware and Systems, 2009, pp. 147–154, Paper identifier (https://doi.org/10.1109/AHS.2009.22).
C.E. Stroud, ”A Designer’s Guide to Built-in Self-Test”, Springer, 2002.
Gayatri Malhotra, Joachim Becker and Maurits Ortmanns, ”Novel Field Programmable Embryonic Cell for Adder and Multiplier”, 9th Conference on Ph.D. Research in Microelectronics and Electronics (PRIME-2013), June 2013.
Z. Zhang and Y. Wang, ”Method to self-repairing reconfiguration
strategy selection of embryonic cellular array on reliability analysis”,
In Proc. of the 2014 NASA/ESA Conference on Adaptive
Hardware and Systems, 2014, pp. 225–232, Paper identifier
(https://doi.org/10.1109/AHS.2014.6880181).
M. F. Torquato and M. A. C. Fernandes, ”High-Performance Parallel Implementation of Genetic Algorithm on FPGA”, Circuits, Systems, and Signal Processing, 38(9), 2019, pp. 4014–4039, Paper identifier
(https://doi.org/10.1007/s00034-019-01037-w).
Z. Zhu, D. J. Mulvaney and V. A. Chouliaras, ”Hardware implementation of a novel genetic algorithm. Neurocomputing”, 71(1–3), 2007, pp. 95–106, Paper identifier (https://doi.org/10.1016/j.neucom.2006.11.031).
A. AL-Marakeby, ”FPGA on FPGA: Implementation of Fine-grained
Parallel Genetic Algorithm on Field Programmable Gate Array”, International Journal of Computer Applications, 80(6), 2013, pp. 29–32, Paper identifier (https://doi.org/10.5120/13867-1725).
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