Abstract
The paper presents an evolutionary approach to the design of fault-tolerant VLSI (very large scale integrated) circuits using EHW (evolvable hardware). The EHW research area comprises a set of applications where GA (genetic algorithms) are used for the automatic synthesis and adaptation of electronic circuits. EHW is particularly suitable for applications requiring changes in task requirements and in the environment or faults, through its ability to reconfigure the hardware structure dynamically and autonomously. This capacity for adaptation is achieved via the use of GA search techniques, in our experiments, a fine-grained CMOS (complementary metal-oxide silicon) FPTA (field-programmable FPGA transistor array) architecture is used to synthesize electronic circuits. The FPTA is a reconfigurable architecture, programmable at the transistor level and specifically designed for EHW applications. The paper demonstrates the power of EA to design analog and digital fault-tolerant circuits. It compares two methods to achieve fault-tolerant design, one based on fitness definition and the other based on population. The fitness approach defines, explicitly, the faults that the component can encounter during its life, and evaluates the average behavior of the individuals. The population approach, on the other hand, uses the implicit information of the population statistics accumulated by the GA over many generations. The paper presents experiment results obtained using both approaches for the synthesis of a fault-tolerant digital circuit (XNOR) and a fault-tolerant analog circuit (multiplier).
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