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Optimization of the control parameters of genetic algorithms is often a time consuming and tedious task. In this work we take the meta-level genetic algorithm approach to control parameter optimization. We enhance this process by incorporating a neural network for fitness evaluation. This neural network is trained to learn the complex interactions of the genetic algorithm control parameters and is used to predict the performance of the genetic algorithm relative to values of these control parameters. To validate our approach we describe a genetic algorithm for the largest common subgraph problem that we develop using this neural network enhanced meta-level genetic algorithm. The resulting genetic algorithm significantly outperforms a hand-tuned variant and is shown to be competitive with a hill-climbing algorithm used in practical applications. 1 Introduction Genetic algorithms use a number of parameters to control their evolutionary search for the solution to thei...

We give the first study of copy detection techniques for VLSI CAD applications; these techniques are complementary to previous watermarking-based IP protection methods in finding and proving improper use of design IP. After reviewing related literature (notably in the text processing domain), we propose a generic methodology for copy detection based on determining basic elements within structural representations of solutions (IPs), calculating (context-independent) signatures for such elements, and performing fast comparisons to identify potential violators of IP rights. We give example implementations of this methodology in the domains of scheduling, graph coloring and gate-level layout; experimental results show the effectiveness of our copy detection schemes as well as the low overhead of implementation. We remark on open research areas, notably the potentially deep and complementary interaction between watermarking and copy detection. 1 Introduction With more functionality integr...

Modern heterogeneous FPGAs contain “hard ” specificpurpose structures such as blocks of memory and multipliers in addition to the completely flexible “soft ” programmable logic and routing. These hard structures provide major benefits, yet raise interesting questions in FPGA CAD and architecture. To develop high-quality CAD mapping algorithms for these structures, and indeed to measure the quality of proposed new structures in the architectural domain, it is essential to have a flexible tool at the RTL synthesis level that permits heterogeneous FPGA CAD and architecture experimentation. In this paper we present a synthesis tool, called Odin, and an algorithm that permits flexible targeting of hard structures in FPGAs. Odin maps Verilog designs to two different FPGA CAD flows: Altera’s Quartus, and the academic VPR CAD flow. We have expended significant effort to make the quality of this tool comparable to an industrial front-end synthesis tool, and we present mapping results for our benchmarks that show the quality of our results. 1.

The problem of finding meaningful subcircuits in a logic layout appears in many contexts in computeraided design. Existing techniques rely upon finding exact matchings of subcircuit structure within the layout. These syntactic techniques fail to identify functionally equivalent subcircuits that are differently implemented, optimized, or otherwise obfuscated. We present a mechanism for identifying functionally equivalent subcircuits that can overcome many of these limitations. Such semantic matching is particularly useful in the field of design recovery. 1 Introduction The identification of meaningful subcircuits within a larger design is of interest in many CAD applications. Of particular interest is the identification of a cluster of connected low-level devices that form a highlevel component. Previous approaches to this problem have relied upon the discovery of subgraph isomorphisms to identify subcircuits [1--3]. While useful in applications such as converting a transistor netlist ...

The PageRank algorithm perturbs the adjacency matrix defined by a set of web pages and hyperlinks such that the resulting matrix is positive and row-stochastic. Applying the Perron-Frobenius theorem establishes that the eigenvector associated with the dominant eigenvalue exists and is unique. For some graphs, the PageRank algorithm may yield a canonical isomorph. We propose a ranking method based on the matrix inverse. Since the inverse may not exist, we apply two isomorphismpreserving perturbations, based on the signless Laplacian, to ensure that the resulting matrix is diagonally dominant. By applying the Gershgorin Circle theorem, we know this matrix must have an inverse, namely, a set of vectors unique up to isomorphism. We concatenate sorted rows of the inverse with its unsorted rows, lexicographically sort on the concatenated matrix, and apply the ranking as an induced permutation on the input adjacency matrix. This preliminary report shows IsoRank identifies most random graphs and always terminates in polynomial time, illustrated by the execution run times for a small set of graphs. IsoRank has been applied to dense graphs of as many as 4,000 vertices.

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We give the first study of copy detection techniques for VLSI CAD applications; these techniques are complementary to previous watermarking-based IP protection methods in finding and proving improper use of design IP. After reviewing related literature (notably in the text processing domain), we propose a generic methodology for copy detection based on determining basic elements within structural representations of solutions (IPs), calculating (context-independent) signatures for such elements, and performing fast comparisons to identify potential violators of IP rights. We give example implementations of this methodology in the domains of scheduling, graph coloring and gate-level layout; experimental results show the effectiveness of our copy detection schemes as well as the low overhead of implementation. We remark on open research areas, notably the potentially deep and complementary interaction between watermarking and copy detection.

Prior research on novelty detection has primarily focused on algorithms to “detect” novelty for a given application domain. Effective storage, indexing and retrieval of novel events (beyond detection) are largely ignored as a problem in itself. In light of the recent advances in counter-terrorism efforts and link discovery initiatives, the need for effective data management of novel events assumes apparent importance. Automatically detecting novel events in video data streams is an extremely challenging task. The aim of this thesis is to provide evidence to the fact that the notion of novelty in video as perceived by a human is extremely subjective and therefore algorithmically illdefined. Though it comes as no surprise that current machine-based parametric learning systems to accurately mimic human novelty perception are far from perfect such systems have recently been very successful in exhaustively capturing novelty in video once the novelty function is well-defined by a human expert. So, how truly effective are these machine based novelty detection systems as compared to human novelty detection? In this paper we outline an experimental evaluation of the human vs machine based novelty systems in terms of qualitative performance. We then quantify this evaluation using a variety of metrics based

This paper describes a method of optimally sizing digital circuits on a static-timing basis. All paths through the logic are considered simultaneously and no input patterns need be specified by the user. The method is unique in that it is based on gradient-based, nonlinear optimization and can accommodate transistor-level schematics without the need for pre-characterization. It employs efficient time-domain simulation and gradient computation for each channel-connected component. A large-scale, general-purpose, nonlinear optimization package is used to solve the tuning problem. A prototype tuner has been developed that accommodates combinational circuits consisting of parameterized library cells. Numerical results are presented. 1 Introduction and previous work