Proceedings of the IEEE 2001. 2nd International Symposium on Quality Electronic Design
Verification of complex behavioral models has become a critical and time-consuming process. Deter... more Verification of complex behavioral models has become a critical and time-consuming process. Determine when to switch to different testing strategy phase is the key to improving efficiency. This paper presents an overview of the existing statistical stopping rules that can be used for behavioral model verification. We examined the stopping rules using two VHDL models for five consecutive test phases.
Proceedings International Symposium on Quality Electronic Design
ABSTRACT In order to improve the effectiveness of simulation-based behavioral verification, it is... more ABSTRACT In order to improve the effectiveness of simulation-based behavioral verification, it is important to determine when to stop the current test strategy and to switch to an expectantly more rewarding test strategy. The location of a stopping point is dependent on the statistical model one chooses to describe the coverage behavior during verification. In this paper, we present dynamic Bayesian (DB) and confidence-based dynamic Bayesian (CDB) stopping rules for behavioral VHDL model verification. The statistical assumptions of the proposed stopping rules are based on experimental evaluation of probability distribution functions and correlation functions. Fourteen behavioral VHDL models were experimented with to determine the high efficiency of the proposed stopping rules over the existing ones. Results show that the DB and the CDB stopping rules outperform all the existing stopping rules with an average improvement of at least 69% in coverage per testing patterns used.
Proceedings Fourth IEEE International Workshop on Computer Architecture for Machine Perception. CAMP'97
Abstract Edges produced by existing edge detection algorithms often contain discontinuities. Edge... more Abstract Edges produced by existing edge detection algorithms often contain discontinuities. Edge linking as a post-processing step is an important step for computer vision and pattern recognition. We present a real-time algorithm and its VLSI implementation for linking ...
Proceedings IEEE 2000 First International Symposium on Quality Electronic Design (Cat. No. PR00525)
Abstract When designing a system in the behavioral level, one of the most important steps to be t... more Abstract When designing a system in the behavioral level, one of the most important steps to be taken is verifying its functionality before it is released to the logic/PD design phase. One may consider behavioral models as oracles in industries to test against when the final chip is produced. In this work, we use branch coverage as a measure for the quality of verifying/testing behavioral models. Minimum effort for achieving a given quality level can be realized by using the proposed stopping rule. The stopping rule guides the process to ...
Proceedings IEEE International High-Level Design Validation and Test Workshop (Cat. No.PR00786), 2000
During behavioral model verification, it is important to determine the stopping pointfor the curr... more During behavioral model verification, it is important to determine the stopping pointfor the current test strategy and for moving to a different test strategy. It has been shown that the location of the stopping point is highly dependent on the statistical model one should choose to describe ...
Proceedings First IEEE International Workshop on Electronic Design, Test and Applications '2002
ABSTRACT Statistically forecasting potential returns in terms of code coverage for a given set of... more ABSTRACT Statistically forecasting potential returns in terms of code coverage for a given set of test cases (patterns) to be applied to a behavioral model can improve the overall effectiveness of behavioral model verification. In this paper, we present a forecasting model for behavioral VHDL model verification. The statistical assumptions of the proposed model are based on experimental evaluation of probability distribution functions and correlation functions. Results show that the forecasting model is of high accuracy. The prediction error of the proposed forecast model in estimating the probability of new coverage is, at most, 2% from the actual probability of having coverage when predicting 1000 simulation cycles into the future. When the prediction window size increases to 10,000 simulation cycles, the expected error in predicting the probability of having coverage is 13%, at most. The marginal error in predicting the waiting time to coverage is less than 30% in forecasting 1000 simulation cycles, and at most ±22% in forecasting 10,000 simulation cycles
Anti-random testing has proved useful in a series of empirical evaluations. The basic premise of ... more Anti-random testing has proved useful in a series of empirical evaluations. The basic premise of anti-random testing is to chose new test vectors that are as far away from existing test inputs as possible. The distance measure is either Hamming distance or Cartesian distance. Unfortunately, this method essentially requires enumeration of the input space and computation of each input vector when used on an arbitrary set of existing test data. This prevents scale-up to large test sets and/or long input vectors.
International Symposium on Quality Electronic Design, 2003
With continuing scaling of CMOS process, process variations in the form of die-to-die and,within-... more With continuing scaling of CMOS process, process variations in the form of die-to-die and,within-die variations become,sig- nificant which,cause timing uncertainty. This paper proposes,a method,of analytically analyzing statistical behavior of multiple coupled,interconnects with an uncertain signal arrival time at each interconnect input (aggressors and the victim). The method utilizes delay change characteristics due to changes in relative ar- rival time
Proceedings of the IEEE 2001. 2nd International Symposium on Quality Electronic Design
Verification of complex behavioral models has become a critical and time-consuming process. Deter... more Verification of complex behavioral models has become a critical and time-consuming process. Determine when to switch to different testing strategy phase is the key to improving efficiency. This paper presents an overview of the existing statistical stopping rules that can be used for behavioral model verification. We examined the stopping rules using two VHDL models for five consecutive test phases.
Proceedings International Symposium on Quality Electronic Design
ABSTRACT In order to improve the effectiveness of simulation-based behavioral verification, it is... more ABSTRACT In order to improve the effectiveness of simulation-based behavioral verification, it is important to determine when to stop the current test strategy and to switch to an expectantly more rewarding test strategy. The location of a stopping point is dependent on the statistical model one chooses to describe the coverage behavior during verification. In this paper, we present dynamic Bayesian (DB) and confidence-based dynamic Bayesian (CDB) stopping rules for behavioral VHDL model verification. The statistical assumptions of the proposed stopping rules are based on experimental evaluation of probability distribution functions and correlation functions. Fourteen behavioral VHDL models were experimented with to determine the high efficiency of the proposed stopping rules over the existing ones. Results show that the DB and the CDB stopping rules outperform all the existing stopping rules with an average improvement of at least 69% in coverage per testing patterns used.
Proceedings Fourth IEEE International Workshop on Computer Architecture for Machine Perception. CAMP'97
Abstract Edges produced by existing edge detection algorithms often contain discontinuities. Edge... more Abstract Edges produced by existing edge detection algorithms often contain discontinuities. Edge linking as a post-processing step is an important step for computer vision and pattern recognition. We present a real-time algorithm and its VLSI implementation for linking ...
Proceedings IEEE 2000 First International Symposium on Quality Electronic Design (Cat. No. PR00525)
Abstract When designing a system in the behavioral level, one of the most important steps to be t... more Abstract When designing a system in the behavioral level, one of the most important steps to be taken is verifying its functionality before it is released to the logic/PD design phase. One may consider behavioral models as oracles in industries to test against when the final chip is produced. In this work, we use branch coverage as a measure for the quality of verifying/testing behavioral models. Minimum effort for achieving a given quality level can be realized by using the proposed stopping rule. The stopping rule guides the process to ...
Proceedings IEEE International High-Level Design Validation and Test Workshop (Cat. No.PR00786), 2000
During behavioral model verification, it is important to determine the stopping pointfor the curr... more During behavioral model verification, it is important to determine the stopping pointfor the current test strategy and for moving to a different test strategy. It has been shown that the location of the stopping point is highly dependent on the statistical model one should choose to describe ...
Proceedings First IEEE International Workshop on Electronic Design, Test and Applications '2002
ABSTRACT Statistically forecasting potential returns in terms of code coverage for a given set of... more ABSTRACT Statistically forecasting potential returns in terms of code coverage for a given set of test cases (patterns) to be applied to a behavioral model can improve the overall effectiveness of behavioral model verification. In this paper, we present a forecasting model for behavioral VHDL model verification. The statistical assumptions of the proposed model are based on experimental evaluation of probability distribution functions and correlation functions. Results show that the forecasting model is of high accuracy. The prediction error of the proposed forecast model in estimating the probability of new coverage is, at most, 2% from the actual probability of having coverage when predicting 1000 simulation cycles into the future. When the prediction window size increases to 10,000 simulation cycles, the expected error in predicting the probability of having coverage is 13%, at most. The marginal error in predicting the waiting time to coverage is less than 30% in forecasting 1000 simulation cycles, and at most ±22% in forecasting 10,000 simulation cycles
Anti-random testing has proved useful in a series of empirical evaluations. The basic premise of ... more Anti-random testing has proved useful in a series of empirical evaluations. The basic premise of anti-random testing is to chose new test vectors that are as far away from existing test inputs as possible. The distance measure is either Hamming distance or Cartesian distance. Unfortunately, this method essentially requires enumeration of the input space and computation of each input vector when used on an arbitrary set of existing test data. This prevents scale-up to large test sets and/or long input vectors.
International Symposium on Quality Electronic Design, 2003
With continuing scaling of CMOS process, process variations in the form of die-to-die and,within-... more With continuing scaling of CMOS process, process variations in the form of die-to-die and,within-die variations become,sig- nificant which,cause timing uncertainty. This paper proposes,a method,of analytically analyzing statistical behavior of multiple coupled,interconnects with an uncertain signal arrival time at each interconnect input (aggressors and the victim). The method utilizes delay change characteristics due to changes in relative ar- rival time
Uploads