FPGA BASED SELF-HEALING STRATEGY FOR SYNCHRONOUS SEQUENTIAL CIRCUITS

The paper develops an efficient mechanism with a view to healing bridging faults in synchronous sequential circuits. The scheme inserts faults randomly into the system at the signal levels, encompasses ways to intrigue the state of the signals and carries it with steps to rig out the true values at the primary output lines. The attempts espouse the ability of the methodology to explore the occurrence of a variety of single and multiple bridging faults and arrive at the true output. The approach enables to detect the occurrence of wired-OR and wired AND bridging faults in the combinational part of the serial binary adder as the CUT and heal both the inter and intra-gate faults through the use of the proposed methodology. It allows claiming a lower area overhead and computationally a sharp increase in the fault coverage area over the existing Triple Modular Redundancy (TMR) technique. The Field Programmable Gate Arrays (FPGA) based Spartan architecture operates through Very High-Speed Integrated Circuit Hardware Description Language (VHDL) to synthesize the Modelsim code for validating the simulation exercises. The claim incites to increase the reliability of the synchronous sequential circuits and espouse a place for the use of the strategy in the digital world

Self-Scaling Evolution of Analog Computation Circuits

Energy and performance improvements of continuous-time analog-based computation for selected applications offer an avenue to continue improving the computational ability of tomorrow*s electronic devices at current technology scaling limits. However, analog computation is plagued by the difficulty of designing complex computational circuits, programmability, as well as the inherent lack of accuracy and precision when compared to digital implementations. In this thesis, evolutionary algorithm-based techniques are utilized within a reconfigurable analog fabric to realize an automated method of designing analog-based computational circuits while adapting the functional range to improve performance. A Self-Scaling Genetic Algorithm is proposed to adapt solutions to computationally-tractable ranges in hardware-constrained analog reconfigurable fabrics. It operates by utilizing a Particle Swarm Optimization (PSO) algorithm that operates synergistically with a Genetic Algorithm (GA) to adaptively scale and translate the functional range of computational circuits composed of high-level or low-level Computational Analog Elements to improve performance and realize functionality otherwise unobtainable on the intrinsic platform. The technique is demonstrated by evolving square, square-root, cube, and cube-root analog computational circuits on the Cypress PSoC-5LP System-on-Chip. Results indicate that the Self-Scaling Genetic Algorithm improves our error metric on average 7.18-fold, up to 12.92-fold for computational circuits that produce outputs beyond device range. Results were also favorable compared to previous works, which utilized extrinsic evolution of circuits with much greater complexity than was possible on the PSoC-5LP

Exploiting development to enhance the scalability of hardware evolution.

Evolutionary algorithms do not scale well to the large, complex circuit design problems typical of the real world. Although techniques based on traditional design decomposition have been proposed to enhance hardware evolution's scalability, they often rely on traditional domain knowledge that may not be appropriate for evolutionary search and might limit evolution's opportunity to innovate. It has been proposed that reliance on such knowledge can be avoided by introducing a model of biological development to the evolutionary algorithm, but this approach has not yet achieved its potential. Prior demonstrations of how development can enhance scalability used toy problems that are not indicative of evolving hardware. Prior attempts to apply development to hardware evolution have rarely been successful and have never explored its effect on scalability in detail. This thesis demonstrates that development can enhance scalability in hardware evolution, primarily through a statistical comparison of hardware evolution's performance with and without development using circuit design problems of various sizes. This is reinforced by proposing and demonstrating three key mechanisms that development uses to enhance scalability: the creation of modules, the reuse of modules, and the discovery of design abstractions. The thesis includes several minor contributions: hardware is evolved using a common reconfigurable architecture at a lower level of abstraction than reported elsewhere. It is shown that this can allow evolution to exploit the architecture more efficiently and perhaps search more effectively. Also the benefits of several features of developmental models are explored through the biases they impose on the evolutionary search. Features that are explored include the type of environmental context development uses and the constraints on symmetry and information transmission they impose, genetic operators that may improve the robustness of gene networks, and how development is mapped to hardware. Also performance is compared against contemporary developmental models

Evolvable Hardware for Space Applications

Abstract. This paper focuses on characteristics and applications of evolvable hardware (EHW) to space systems. The motivation for looking at EHW originates in the need for more autonomous adaptive space systems. The idea of evolvable hardware becomes attractive for long missions when the hardware looses optimality, and uploading new software only partly alleviates the problem if the computing hardware becomes obsolete or the sensing hardware faces needs outside original design specifications. The paper reports the first intrinsic evolution on an analog ASIC (a custom analog neural chip), suggests evolution of dynamical systems in state-space representations, and demonstrates evolution of compression algorithms with results better than the best-known compression algorithms.

Evolvable Hardware for Space Applications

This article surveys the research of the Evolvable Systems Group at NASA Ames Research Center. Over the past few years, our group has developed the ability to use evolutionary algorithms in a variety of NASA applications ranging from spacecraft antenna design, fault tolerance for programmable logic chips, atomic force field parameter fitting, analog circuit design, and earth observing satellite scheduling. In some of these applications, evolutionary algorithms match or improve on human performance