Figure 5 - uploaded by Daniel Mange
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Modulo-4 up-down counter. (a) Ordered binary decision diagrams for Q1+ and Q0+. (b) Multiplexer diagram using MUXTREE ® molecules. (c) 6-molecule cell. [RG: ribosomic genome]
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As new techniques for applying biological processes to the development of computer hardware reach maturity, the EMBRYONICS (for embryonic electronics) project gains in complexity and flexibility of use. New features, such as the capability to more efficiently store data, have lately been added to MUXTREE® (for multiplexer tree) - our electronic mol...
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... us consider a simple example of artificial organism, a single cell ( Figure 5) realizing a modulo-4 up-down counter, defined by the following sequences: ...
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... for C=0 (counting up): Q1Q0 = 00 → 01 → 10 → 11 → 00 → … ♦ for C=1 (counting down): Q1Q0 = 00 → 11 → 10 → 01 → 00 → … It can be verified that the two ordered binary decision diagrams Q1+ and Q0+ of Figure 5a (where each test element is represented by a diamond with a single output, a "true" input, and a "complemented" input identified by a small circle) represent a possible realization of the counter [3][4]. The leaf elements, represented as squares, define the output values of the given functions (Q1+ and Q0+ in the example) defined by the following equations: Q1+ = C·(Q1·Q0+Q1'·Q0')+C'·(Q1·Q0'+Q1'·Q0) Q0+ = Q0' The direct implementation of the ordered binary decision diagrams on silicon follows the layout from Figure 5b. ...
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... for C=0 (counting up): Q1Q0 = 00 → 01 → 10 → 11 → 00 → … ♦ for C=1 (counting down): Q1Q0 = 00 → 11 → 10 → 01 → 00 → … It can be verified that the two ordered binary decision diagrams Q1+ and Q0+ of Figure 5a (where each test element is represented by a diamond with a single output, a "true" input, and a "complemented" input identified by a small circle) represent a possible realization of the counter [3][4]. The leaf elements, represented as squares, define the output values of the given functions (Q1+ and Q0+ in the example) defined by the following equations: Q1+ = C·(Q1·Q0+Q1'·Q0')+C'·(Q1·Q0'+Q1'·Q0) Q0+ = Q0' The direct implementation of the ordered binary decision diagrams on silicon follows the layout from Figure 5b. Each test element is implemented with one MUXTREE ® molecule, keeping the same interconnection diagram and assigning the values of the leaf elements to the appropriate multiplexer inputs. ...
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... cell implementing the counter has therefore 3 rows and 2 columns of MUXTREE ® molecules. From the multiplexer diagram of Figure 5b and from the description of the MUXTREE ® molecule in logic mode (Figure 3 and Figure 4) we can compute the control bits of each molecular code, finally generating the MolCodes of Figure 5c, each MolCode being a word of six hexadecimal digits (00QM, CREG19:16, CREG15:12, CREG11:8, CREG7:4, and CREG3:0). This string of MolCodes is our ribosomic genome RG [3]. ...
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... cell implementing the counter has therefore 3 rows and 2 columns of MUXTREE ® molecules. From the multiplexer diagram of Figure 5b and from the description of the MUXTREE ® molecule in logic mode (Figure 3 and Figure 4) we can compute the control bits of each molecular code, finally generating the MolCodes of Figure 5c, each MolCode being a word of six hexadecimal digits (00QM, CREG19:16, CREG15:12, CREG11:8, CREG7:4, and CREG3:0). This string of MolCodes is our ribosomic genome RG [3]. ...
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... the microprogram needs only 42 words, the last memory entries, from address 2A to address 2F, repeat the last instruction (DATA=7=end). At configuration time, the string of MolCodes, defined as the ribosomic genome RG, enters the molecules following the path shown to Figure 5c (the memory mode molecules are configured as any other molecule in the array). At operating time, because of the values of the MEM2:0 bits, the actual connections within the memory structure are those shown in Figure 13b. ...
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... this particular case, m signifies the number of organisms along the horizontal coordinate, and n signifies the number of organisms along the vertical coordinate (i.e., m=2 and n=1). The ribosomic genome RG is described in Subsection 3.2 (Figure 5c). In the actual implementation, we add a spare column at the right of each organism. ...
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... One such important process is the development of the individual from a single fertilised egg (zygote) through its repeated division and differentiation. Embryonics (embryonic electronics)12345 tries to adapt and transpose the development of such processes and living characteristics of organisms to the world of silicon integrated circuits. Systems are built by a homogenous array of identical cells similarly to that of commercial FPGAs, but they posses self-replication, self-repair and fault-tolerant properties [6]. ...
... The circuit will divide the input frequency (F) by a pre-settable 3-bit number (DS). For comparison purposes the same logic and switch block design was used as proposed by Prodan and Ortega [2, 12]. Self-check in each cell of the embryonic array is performed by BIST (Built-In Self-Test) logic proposed by Mange [13]. ...
The unique characteristic of a biological system in nature is described by its DNA through the transcription of its genes. This is like a memory map that each cell of an organism contains. An artificial embryonic cell contains a similar memory map where the specific 'gene' it executes determines the functionality of the cell. An electronic system is then constructed by a large number of identical cells where each possesses a different behaviour. The cells collectively however determine the characteristic of the target system. This paper proposes a new, variable size memory map based on a novel gene selection algorithm that no longer uses the hitherto common address decoding approach to access the cell's gene it will execute. Instead it applies the principle of cyclic metamorphic gene selection of the artificial DNA memory. A further benefit of the approach is that through genetic operators or variable memory space environment for enhanced behaviour the functionality of the system can also be easily altered.
... Embryonics (embryonic electronics)12345 was originally proposed in the mid-90's by the Swiss Federal Institute of Technology to design reliable electronic systems. It tries to adapt and transpose the development of living characteristics of organisms to the world of silicon integrated circuits. ...
... The circuit will divide the input frequency (F) by a pre-settable 3-bit number (DS). For comparison purposes the same logic and switch block design was used as proposed by Prodan and Ortega [2, 12]. Self-check in each cell of the embryonic array is performed by BIST (Built-In Self-Test) logic proposed by Mange [13]. ...
Living beings in nature are made up of structurally identical cells, where each cell contains a copy of its DNA, the unique characteristic of the individual. This DNA is like a memory. It remembers and defines the behaviour of the individual and remains constant throughout its life time. An artificial embryonic cell contains a similar memory map where the specific 'gene' it executes determines the functionality of the cell. An electronic system is then constructed by interconnecting a large number of identical cells. Each cell, similarly to nature, executes only a segment (gene) of the DNA and thus they all demonstrate different behaviour. However these cells will collectively determine the characteristic and configuration of the target system. This paper proposes a novel gene selection algorithm that doesn't use the common address decoding approach and provides easier reconfiguration of systems for a different behaviour.
As aerospace vehicles travel in a hellish environment, the reliability of the measuring and controlling systems has played a critical role in the credibility of a whole airborne system. Embryo-electronic system is a bionic hardware capable of self-diag- nosing and self-healing. This article presents a new approach to design embryo-electronic systems and introduces their bionic principles, system structures and fault-tolerant mechanism. As the current methods cannot meet the requirements for large-scale embryo-electronic systems, this article advances a new shift-register-based configuration memory of embryonic system to solve the problem by using the inter-cell communication to reduce the gene storage capacity of a single cell. The article designs an overall structure of the shift-register-based configuration memories of the embryonic system and connects them into a chain structure. The article also designs an inner circuit of the cell, the control of shift-register-based configuration memory and the way of runtime dynamic configuration. The simulation of field programmable gate array (FPGA) evidences the realizability of the proposed design. Compared to the SRAM-based one, this memory can save 90% of the area when constructing embryonic systems larger than 128 × 128 under the same condition.
In the quest of designing extremely fault-tolerant computing systems drawing inspiration from nature is one avenue worth exploring.
Embryonics (embryonic electronics) is a research project that attempts to implement features otherwise available in the world
of biology to design robust, massively parallel arrays of processors. This paper elaborates on some of the design approaches
undertaken in order to ensure a high level of fault-tolerance as well as on how to partition the array in order to optimally
make use of spare resources.
In nature the DNA contained in each cell of an organism, is in fact a memory map that, through the transcription of its genes,
describes the unique characteristics of the individual. Similarly in an artificial embryonic cell, used to construct electronic
systems, a memory map and its relevant gene also describes and determines the functionality of each cell and, collectively,
the entire system. This paper proposes a new variable size memory map based on a novel and efficient gene selection algorithm
that no longer uses the hitherto common address decoding approach to access some fixed memory space. Instead, it applies the
principle of cyclic metamorphic gene selection of the artificial DNA memory. A further benefit of the approach is that the
functionality of the system can also be easily altered through genetic operators or variable memory space environment for
enhanced behaviour. It is suitable therefore for the implementation of GA processes.
This paper introduces a novel design approach for embryonic arrays that is able to employ all three axial characteristics of living beings: phytogeny, ontogeny, and epigenesis. Rather than using multiplexer based binary decision diagrams (BDD) it utilizes the K/N digital neurons to define the functionality of the cells. A special neural network design technique that uses Voronoi diagram (VoD) will be employed that enables neural network array-like configuration of embryonic arrays. Additionally, learning ability, geometrical extraction of genome, gate level configuration, large number of minterm and sum of products (SOP) generation for its constituent cells are some of the other advantages of the technique.
Space applications endorse the quest for dependable computing as a vital requirement instead of an expendable quality indicator. A novel direction in designing digital systems with superior dependability is based on bio-inspiration. As a key representative, the Embryonics project implements bio-inspired robustness, the backbone of its fault tolerance being a two level hierachical self-repair. However, the outer space radiation-hard environment is especially harsh to electronic devices, causing a variety of errors (soft fails). The memory structures employed by Embryonics cannot be protected by its self-repairing mechanism. Therefore we investigate the causes and influences of soft fails over the current Embryonics framework and propose a solution for the identified vulnerability based on error correcting codes. We also provide a reliability comparison between memory structures with and without error correcting codes.
