Karen Panetta Lentz

Concurrent min-max simulation

Parametric process variations, which are inherent in the manufacture of complex digital circuits, can cause variations in the timing characteristics of a digital device. These device timing variations can cause catastrophic failures to the intended logical operation of the whole design. Min-max timing simulation is simulation technique which is well suited to verify that a given design functions correctly, even under the influence of parametric process variations. Unfortunately, in the past, min-max timing simulation has been very expensive in simulation CPU time and in the amount of memory consumed. The authors present a technique, concurrent min-max simulation (CMMS), which employs the techniques developed in concurrent fault simulation, to elegantly solve the min-max timing simulation problem.<<ETX>>

Visualization using rational morphology and zonal magnitude reduction

Morphological filters are investigated and employed for detecting and visualizing objects within an image. The techniques developed here will be employed on NASAs Earth Observing System (EOS) satellite data products for the purpose of anomaly detection. Previous efforts have shown the phase information in the spectral domain to be more significant than the magnitude information in representing the location of objects in an image. The magnitude information does provide some useful information for object location, but it is also sensitive to image illumination, blurring, and magnification variations, all of which influence the performance of object detection algorithms. Magnitude reduction techniques in the spectral domain can dramatically improve subsequent object detection methods by causing them to rely less on the magnitude and more on the phase information of the image. However, magnitude reduction enhances the high-frequency noise within an image, often causing unwanted noise to be interpreted as image objects. We propose three new techniques for improved object detection and noise reduction. Our first method employs varying magnitude reductions within radially concentric zones, using increasingly greater reductions in higher frequency zones. By employing this zonal magnitude- reduction technique, we manage to attenuate the high-frequency noise component while still maintaining the improved visualization performance of the magnitude reduction method. Our second technique operates by utilizing several magnitude reductions of varying scale, performing object detection on each magnitude-reduced image, and combining the results for improved accuracy. This result-averaging method allows us to further reduce our false-alarm rate from high-frequency noise while increasing visualization performance. Our third method is a new technique which is based on the ratios of morphological filters. By combining classical morphological filters in this way, we are able to produce more robust results which can yield useful information as to the location of image objects.

Multiple Experiment Environments for Testing

Concurrent simulation (CS) has been used successfully as areplacement for serial simulation. Based on storing differences fromexperiments, CS saves storage, speeds up simulation time and allowsexcellent internal observation of events. In this paper, we introduceMultiple Domain Concurrent Simulation (MDCS) which like concurrentsimulation, maintains efficiency by only simulating differences. MDCS alsoallows experiments to interact with one another and create new experimentsthrough the use of domains. These experiments can be traced and observed atany point, providing insight into the origin and causes of new experiments.While many experiment scenarios can be created, MDCS uses dynamic spawningand experiment compression rather than explicit enumeration to ensure thatthe number of experiment scenarios does not become exhaustive. MDCS does notrequire any pre-analysis or additions to the circuit under test. Providingthis capability in digital logic simulators allows more test cases to be runin less time. MDCS gives the exact location and causes of every experimentbehavior and can be used to track the signature paths of test patterns forcoverage analysis.We will describe the algorithms for MDCS, discuss the rules forpropagating experiments and describe the concepts of domains for makingdynamic interactions possible. We will report on the effectiveness of MDCSfor attacking an exhaustive simulation problem such as Multiple Stuck-atFault simulations for digital logic. Finally, the applicability of MDCS formore general experimentation of digital logic systems will be discussed.

Visualization using rational morphology and magnitude reduction II

Morphological filters are investigated and employed for detecting and visualizing objects within an image. The techniques developed here will be employed on NASAs Earth Observing System (EOS) satellite data products for the purpose of anomaly detection. Previous efforts have shown the phase information in the spectral domain to be more significant than the magnitude information in representing the location of objects in an image. The magnitude information does provide some useful information for object location, but it is also sensitive to image illumination, blurring, and magnification variations, all of which influence the performance of object detection algorithms. Magnitude reduction techniques in the spectral domain can dramatically improve subsequent object detection methods by causing them to rely less on the magnitude and more on the phase information of the image. We propose three new improvements to our object enhancement and detection techniques. Our first method is an enhancement to our previous magnitude-reduction technique. Our second improvement is a modification of our Rational Morphological Filters in which we raise our resulting image to a power, thereby magnifying our feature detection capability. Third, we look at speed enhancement by utilizing Hartley and Walsh Transforms in place of classical Fourier techniques.

Handling behavioral components in multi-level concurrent fault simulation

System level modeling is becoming a necessity in all areas of engineering design. As systems grow in complexity, designers may increasingly rely on commercial off-the-shelf (COTS) components. Frequently, these components are described at a high level of abstraction (behaviorally) that complicates fault testing. We discuss the trade-offs of using behavioral components in a design, specifically as it relates to fault simulation. We investigate important issues such as timing, and examine the need to internally-fault behavioral models. We then present our fault-level concurrent fault simulator (MCS) that can accept any combination of gate level and behavioral models using a single kernel. Our kernel propagates faults through behavioral components deterministically. Finally, we present performance results of multi-level models to demonstrate the simulators capabilities and performance.

The comparative and concurrent simulation of discrete-event experiments

Discrete-Event Simulation is a powerful, but underexploited alternative for many kinds of physical experimentation. It permits what is physically impossible or unaffordable, to conduct and run related experiments in parallel, against each other. Comparative and Concurrent Simulation (CCS) is a parallel experimentation method that adds a comparative dimension to discrete-event simulation. As a methodology or style, CCS resembles a many-pronged rake; its effectiveness is proportional to the number of prongs—the number of parallel experiments. It yields information in parallel and in time order, rather than in the arbitrary order of one-pronged serial simulations. CCS takes advantage of the similarities between parallel experiments via the one-for-many simulation of their identical parts; if many experiments are simulated, then it is normally hundreds to thousands times faster than serial simulation. While CCS is a one-dimensional method, a more general, multi-dimensional or multidomain version is MDCCS. MDCCS permits parent experiments to interact and produce offspring experiments, i.e., to produce more, but smaller experiments, and many zero-size/zero-cost experiments. MDCCS is more general, informative, and faster (usually over 100:1) than CCS for most applications. It handles more complex applications and experiments, such as multiple faults, variant executions of a software program, animation, and others.

System verification using multilevel concurrent simulation

The verification of multilevel designs in a single simulator environment can be achieved efficiently using concurrent simulation. MCS is a research simulation tool developed in conjunction with Compaq Computer Corporation and Draper Laboratories. MCS overcomes limitations imposed by merged simulator approaches. MCS achieves this by incorporating techniques that are not specific to any abstraction level, making it attractive for testing interface interconnects and mixed-mode logic. We describe our approach, which is a cohesive simulator platform based on concurrent simulation algorithms.

Multiple-domain concurrent and comparative simulation

Multiple-domain concurrent and comparative simulation (MDCCS), a generalization of concurrent and comparative simulation (CCS), and thus of discrete event simulation, is introduced. MDCCS is a generalization that makes concurrent simulation applicable to virtually every task or problem that can be handled with discrete event simulation.<<ETX>>

TUFTsim: a multiple domain simulator for experimentation

TUFTsim is a multilevel multiple domain concurrent simulator that is able to investigate and detect scenarios of behavior. A scenario is a combination of individual behavior or points of failure. As a behavior propagates through a model, it has the possibility of colliding into another behavior at a common node or juncture. The result of a collision may produce a new combined behavior. The inherent features of concurrent simulation provides the simulator with the ability to track fault signatures, while simultaneously providing experiment observation capabilities. In this paper we present the algorithms that allow scenarios of experiments to be efficiently created and simulated. Using single stuck-at faults, we discuss the issues of dynamically creating multiple stuck-at-fault scenarios.

Multi-level concurrent simulation

As the size and complexity of logic designs become increasingly large, computing resources to verify the correctness of systems on a chip and develop quality test patterns for manufacturing are becoming strained. Using behavioral models in simulation captures the functional characteristics of a design block without necessarily relying on a specific implementation. Models can be interchanged or replaced by abstracted models as more detailed models become available or as more high level system testing is required. This will allow larger systems to be simulated as a cohesive unit. In addition, by utilizing function lists to dynamically create faulty behaviors, we will demonstrate its versatility for fault simulating multilevel models. In this paper, we investigate behavioral fault simulation and discuss the architecture that provides greater accuracy for a more thorough system level simulation.