Hongliang Chang

Bio: Hongliang Chang is an academic researcher from University of Minnesota. The author has contributed to research in topics: Statistical static timing analysis & Probability distribution. The author has an hindex of 6, co-authored 8 publications receiving 1503 citations. Previous affiliations of Hongliang Chang include Cadence Design Systems.

Statistical Timing Analysis Considering Spatial Correlations using a Single Pert-Like Traversal

Abstract: We present an efficient statistical timing analysis algorithm that predicts the probability distribution of the circuit delay while incorporating the effects of spatial correlations of intra-die parameter variations, using a method based on principal component analysis. The method uses a PERT-like circuit graph traversal, and has a run-time that is linear in the number of gates and interconnects, as well as the number of grid partitions used to model spatial correlations. On average, the mean and standard deviation values computed by our method have errors of 0.2% and 0.9%, respectively, in comparison with a Monte Carlo simulation.

Statistical timing analysis under spatial correlations

Abstract: Process variations are of increasing concern in today's technologies, and they can significantly affect circuit performance An efficient statistical timing analysis algorithm that predicts the probability distribution of the circuit delay considering both inter-die and intra-die variations, while accounting for the effects of spatial correlations of intra-die parameter variations, is presented The procedure uses a first-order Taylor series expansion to approximate the gate and interconnect delays Next, principal component analysis (PCA) techniques are employed to transform the set of correlated parameters into an uncorrelated set The statistical timing computation is then easily performed with a program evaluation and review technique (PERT)-like circuit graph traversal The run time of this algorithm is linear in the number of gates and interconnects, as well as the number of varying parameters and grid partitions that are used to model spatial correlations The accuracy of the method is verified with Monte Carlo (MC) simulation On average, for the 100 nm technology, the errors of mean and standard deviation (SD) values computed by the proposed method are 106% and -434%, respectively, and the errors of predicting the 99% and 1% confidence point are -246% and -099%, respectively A testcase with about 17 800 gates was solved in about 500 s, with high accuracy as compared to an MC simulation that required more than 15 h

Full-chip analysis of leakage power under process variations, including spatial correlations

Abstract: In this paper, we present a method for analyzing the leakage current, and hence the leakage power, of a circuit under process parameter variations that can include spatial correlations due to intra-chip variation. A lognormal distribution is used to approximate the leakage current of each gate and the total chip leakage is determined by summing up the lognormals. In this work, both subthreshold leakage and gate tunneling leakage are considered. The proposed method is shown to be effective in predicting the CDF/PDF of the total chip leakage. The average errors for mean and sigma values are -1.3% and -4.1%.

Parameterized block-based statistical timing analysis with non-gaussian parameters, nonlinear delay functions

Abstract: Variability of process parameters makes prediction of digital circuit timing characteristics an important and challenging problem in modern chip design. Recently, statistical static timing analysis (statistical STA) has been proposed as a solution. Unfortunately, the existing approaches either do not consider explicit gate delay dependence on process parameters (Liou, et al., 2001), (Orshansky, et al., 2002), (Devgan, et al., 2003), (Agarwal, et al., 2003) or restrict analysis to linear Gaussian parameters only (Visweswariah, et al., 2004), (Chang, et al., 2003). Here the authors extended the capabilities of parameterized block-based statistical STA (Visweswariah, et al., 2004) to handle nonlinear function of delays and non-Gaussian parameters, while retaining maximum efficiency of processing linear Gaussian parameters. The novel technique improves accuracy in predicting circuit timing characteristics and retains such benefits of parameterized block-based statistical STA as an incremental mode of operation, computation of criticality probabilities and sensitivities to process parameter variations. The authors' technique was implemented in an industrial statistical timing analysis tool. The experiments with large digital blocks showed both efficiency and accuracy of the proposed technique.

Design of mixed-signal systems-on-a-chip

Abstract: The electronics industry is increasingly focused on the consumer marketplace, which requires low-cost high-volume products to be developed very rapidly. This, combined with advances in deep submicrometer technology have resulted in the ability and the need to put entire systems on a single chip. As more of the system is included on a single chip, it is increasingly likely that the chip will contain both analog and digital sections. Developing these mixed-signal (MS) systems-on-chip presents enormous challenges both to the designers of the chips and to the developers of the computer-aided design (CAD) systems that are used during the design process. This paper presents many of the issues that act to complicate the development of large single-chip MS systems and how CAD systems are expected to develop to overcome these issues.

First-order incremental block-based statistical timing analysis

Abstract: Variability in digital integrated circuits makes timing verification an extremely challenging task. In this paper, a canonical first order delay model is proposed that takes into account both correlated and independent randomness. A novel linear-time block-based statistical timing algorithm is employed to propagate timing quantities like arrival times and required arrival times through the timing graph in this canonical form. At the end of the statistical timing, the sensitivities of all timing quantities to each of the sources of variation are available. Excessive sensitivities can then be targeted by manual or automatic optimization methods to improve the robustness of the design. This paper also reports the first incremental statistical timer in the literature which is suitable for use in the inner loop of physical synthesis or other optimization programs. The third novel contribution of this paper is the computation of local and global criticality probabilities. For a very small cost in CPU time, the probability of each edge or node of the timing graph being critical is computed. Numerical results are presented on industrial ASIC chips with over two million logic gates.

First-Order Incremental Block-Based Statistical Timing Analysis

Abstract: Variability in digital integrated circuits makes timing verification an extremely challenging task. In this paper, a canonical first-order delay model that takes into account both correlated and independent randomness is proposed. A novel linear-time block-based statistical timing algorithm is employed to propagate timing quantities like arrival times and required arrival times through the timing graph in this canonical form. At the end of the statistical timing, the sensitivity of all timing quantities to each of the sources of variation is available. Excessive sensitivities can then be targeted by manual or automatic optimization methods to improve the robustness of the design. This paper also reports the first incremental statistical timer in the literature, which is suitable for use in the inner loop of physical synthesis or other optimization programs. The third novel contribution of this paper is the computation of local and global criticality probabilities. For a very small cost in computer time, the probability of each edge or node of the timing graph being critical is computed. Numerical results are presented on industrial application-specified integrated circuit (ASIC) chips with over two million logic gates, and statistical timing results are compared to exhaustive corner analysis on a chip design whose hardware showed early mode timing violations

An Architecture for Fault-Tolerant Computation with Stochastic Logic

Abstract: Mounting concerns over variability, defects, and noise motivate a new approach for digital circuitry: stochastic logic, that is to say, logic that operates on probabilistic signals and so can cope with errors and uncertainty. Techniques for probabilistic analysis of circuits and systems are well established. We advocate a strategy for synthesis. In prior work, we described a methodology for synthesizing stochastic logic, that is to say logic that operates on probabilistic bit streams. In this paper, we apply the concept of stochastic logic to a reconfigurable architecture that implements processing operations on a datapath. We analyze cost as well as the sources of error: approximation, quantization, and random fluctuations. We study the effectiveness of the architecture on a collection of benchmarks for image processing. The stochastic architecture requires less area than conventional hardware implementations. Moreover, it is much more tolerant of soft errors (bit flips) than these deterministic implementations. This fault tolerance scales gracefully to very large numbers of errors.