31983639
Convexity-based algorithms for design centering
14376041
Feasible region approximation using convex polytopes
A new technique for polytope approximation of the feasible region for a design is presented. This method is computationally less expensive than the simplicial approximation method. Results on several circuits are presented. It is shown that the quality of the polytope approximation is substantially better than an ellipsoidal approximation.<<ETX>>
13548202
Noise constrained power optimization for dual V/sub T/ domino logic
In dual threshold voltage techniques, significant subthreshold leakage current is one of the most important design problems. When dual threshold voltage is applied to the domino logic, noise immunity has to be carefully considered because the significant subthreshold current makes dynamic nodes much more susceptible to noise. In this paper, an analytical model for proper keeper transistor sizing to meet noise constraint is presented. Based on the same noise constraint, we propose dual threshold voltage domino logic technique to save power consumption.
13489029
An integrated approach to realistic worst-case design optimization of MOS analog circuits
The authors present a new integrated approach for the optimization of MOS analog circuit performance by using realistic worst-case device parameter files, each corresponding to a performance measure. Nonlinear response surfaces are constructed for the performance measures of interest, and the worst-case device parameter files are identified by solving a set of suitably cast nonlinear programming problems. The worst-case files are shown to depend on the values of the designable parameters. An efficient method of incorporating this dependence during worst-case design optimization has been developed. This method enables the design of circuits with optimal performance and high parametric yields. Some illustrative analog circuit examples are given to demonstrate the application of the worst-case design optimization procedure.<<ETX>>
44275614
Cell-level placement for improving substrate thermal distribution
The dramatic increase of power consumption in very large scale integration circuits has led to high operating temperature and large thermal gradient, thereby resulting in serious timing and reliability concerns. Temperature-tracking is thus becoming of paramount importance in modern electronic design automation (EDA) tools. In this paper we present two thermal placement tools for standard cell and macro cell design styles respectively. They are aimed at reducing hot spots in a design without compromising traditional design metrics such as area and wire length. We developed a compact substrate thermal model that can be used by the placer to calculate the temperature profile of a placement efficiently, or to convert the user-specified temperature constraint into the corresponding power distribution constraint as an alternative placement objective. As a result, our method is much more efficient than directly employing temperature profile simulation during the placement process. The simulation results show noticeable improvement of thermal distribution over the traditional placement algorithm, with little impact on area and wire length of the final layout.
40491047
Domino logic synthesis based on implication graph
In this paper, we present a new approach to the problem of inverter elimination in domino logic synthesis. A small piece of static CMOS logic is introduced to the circuit to avoid significant area penalty resulting from duplication. To maximize the domino logic part and to minimize the static CMOS logic part, a generalized automatic test pattern generation (ATPG)-based logic transformation is proposed to eliminate or relocate a target inverter. Based on the new concept of dominating set of mandatory assignment (DSMA) and the corresponding implication graph, we propose algorithms to identify a minimum candidate set for a target inverter. Experimental results show that logic transformation based on the implication graph can reduce transistor counts by 25% on average, while the delay increases less than 3%.
9325323
A Parallel Feature Tracker for Extended Image Sequences
This paper presents a feature tracker for long image sequences based on simultaneously estimating the motions and deformations of a collection of adjacent image patches. By sharing common corner nodes, the patches achieve greater stability than independent patch trackers. Modeling full bilinear deformations enables tracking in sequences which have large nontranslational motions and/or foreshortening effects. We demonstrate the advantages of our technique with respect to previous algorithms using experimental results.
17171711
Characterization of errors in compositing panoramic images
In this paper we describe the effect of errors in the intrinsic camera parameters on reconstructed panoramic images. A panoramic image is created by first capturing a sequence of images while rotating the camera about a vertical axis a full 360/spl deg/. The subsequent steps are projecting the original rectilinear images onto cylindrical surfaces and compositing them to form the panoramic image. Our analysis has led to a technique that allows simultaneous recovery of the camera focal length and properly composited panoramic images. The correct focal length can be determined by iterating the processes of projecting the original rectilinear images onto cylindrical surfaces given an estimate of the focal length and compositing the resulting images to yield an increasingly better estimate of the focal length. This paper shows that the convergence towards the correct focal length is exponential.
5958118
Delayed stability and performance of distributed congestion control
Recent research efforts to design better Internet transport protocols combined with scalable Active Queue Management (AQM) have led to significant advances in congestion control. One of the hottest topics in this area is the design of discrete congestion control algorithms that are asymptotically stable under heterogeneous feedback delay and whose control equations do not explicitly depend on the RTTs of end-flows. In this paper, we show that max-min fair congestion control methods with a stable symmetric Jacobian remain stable under arbitrary feedback delay (including heterogeneous directional delays) and that the stability condition of such methods does not involve any of the delays. To demonstrate the practicality of the obtained result, we change the original controller in Kelly's work [14] to become robust under random feedback delay and fixed constants of the control equation. We call the resulting framework Max-min Kelly Control (MKC) and show that it offers smooth sending rate, exponential convergence to efficiency, and fast convergence to fairness, all of which make it appealing for future high-speed networks.
16190492
Noise Estimation from a Single Image
In order to work well, many computer vision algorithms require that their parameters be adjusted according to the image noise level, making it an important quantity to estimate. We show how to estimate an upper bound on the noise level from a single image based on a piecewise smooth image prior model and measured CCD camera response functions. We also learn the space of noise level functions how noise level changes with respect to brightness and use Bayesian MAP inference to infer the noise level function from a single image. We illustrate the utility of this noise estimation for two algorithms: edge detection and featurepreserving smoothing through bilateral filtering. For a variety of different noise levels, we obtain good results for both these algorithms with no user-specified inputs.
956323
Estimating Intrinsic Images from Image Sequences with Biased Illumination
We present a method for estimating intrinsic images from a fixed-viewpoint image sequence captured under changing illumination directions. Previous work on this problem reduces the influence of shadows on reflectance images, but does not address shading effects which can significantly degrade reflectance image estimation under the typically biased sampling of illumination directions. In this paper, we describe how biased illumination sampling leads to biased estimates of reflectance image derivatives. To avoid the effects of illumination bias, we propose a solution that explicitly models spatial and temporal constraints over the image sequence. With this constraint network, our technique minimizes a regularization function that takes advantage of the biased image derivatives to yield reflectance images less influenced by shading.