1385145
Simulation based deadlock analysis for system level designs
In the design of highly complex, heterogeneous, and concurrent systems, deadlock detection and resolution remains an important issue. In this paper, we systematically analyze the synchronization dependencies in concurrent systems modeled in the Metropolis design environment, where system functions, high level architectures and function-architecture mappings can be modeled and simulated. We propose a data structure called the dynamic synchronization dependency graph, which captures the runtime (blocking) dependencies. A loop-detection algorithm is then used to detect deadlocks and help designers quickly isolate and identify modeling errors that cause the deadlock problems. We demonstrate our approach through a real world design example, which is a complex functional model for video processing and a high level model of function-architecture mapping.
902904
Automatic Generation of Simulation Monitors from Quantitative Constraint Formula
System design methodology is poised to become the next big enabler for highly sophisticated electronic products. Design verification continues to be a major challenge and simulation will remain an important tool for making sure that implementations perform as they should. In this paper we present algorithms to automatically generate C++ checkers from any formula written in the formal quantitative constraint language, Logic Of Constraints (LOC). The executable can then be used to analyze the simulation traces for constraint violation and output debugging information. Different checkers can be generated for fast analysis under different memory limitations. LOC is particularly suitable for specification of system level quantitative constraints where relative coordination of instances of events, not lower level interaction, is of paramount concern. We illustrate the usefulness and efficiency of our automatic trace analysis methodology with case studies on large simulation traces from various system level designs.
1333400
Utilizing formal assertions for system design of network processors
System level modeling with executable languages such as C/C++ has been crucial in the development of large electronic systems from general processors to application specific designs. To make sure that the executable models behave as they should, the designers often have to "eye-ball" the simulation traces and at best, apply simple "assert" statements or write simple trace checkers in some scripting languages. The problem is the lack of a concise and formal method to specify and check desired properties, whether they be functional or performance in nature. In this paper, we apply assertion checking methodology to the system design of network processors. Functional and performance assertions, based on linear temporal logic and logic of constraints, are written during the design process. Trace checkers and simulation monitors are automatically generated to validate particular simulation runs or to analyze their performance characteristics. Several categories of assertions are checked throughout the design process, such as equivalence, functionality, transaction, and performance. We demonstrate that the assertion-based methodology is very useful for both system level verification and design exploration.
5884737
Logic of constraints: a quantitative performance and functional constraint formalism
In the era of billion-transistor design, it is critical to establish effective verification methodologies from the system level, all the way down to the implementations. In this paper, we introduce logic of constraints (LOC), a logic that is particularly suited to express quantitative performance constraints as well as functional constraints. We analyze the expressiveness of LOC and show that it is important and different from linear temporal logic, upon which traditional hardware assertion languages (e.g., PSL and OpenVera) are based. We propose an automatic simulation trace checking/runtime monitoring methodology that can be used to verify system designs very efficiently. Since a subset of LOC is decidable, we also discuss the formal verification approach for LOC formulas. Through several industrial case studies, we demonstrate the usefulness of the LOC formalism and the corresponding simulation and verification approach at the higher transaction level of abstraction.
42417262
On the Minimum Common Integer Partition Problem
We introduce a new combinatorial optimization problem in this paper, called the Minimum Common Integer Partition (MCIP) problem, which was inspired by computational biology applications including ortholog assignment and DNA fingerprint assembly. A partition of a positive integer n is a multiset of positive integers that add up to exactly n, and an integer partition of a multiset S of integers is defined as the multiset union of partitions of integers in S. Given a sequence of multisets S1, ⋯, Sk of integers, where k ≥ 2, we say that a multiset is a common integer partition if it is an integer partition of every multiset Si, 1≤ i≤ k. The MCIP problem is thus defined as to find a common integer partition of S1, ⋯, Sk with the minimum cardinality. It is easy to see that the MCIP problem is NP-hard since it generalizes the well-known Set Partition problem. We can in fact show that it is APX-hard. We will also present a $\frac{5}{4}$-approximation algorithm for the MCIP problem when k = 2, and a $\frac{3k(k-1)}{3k-2}$-approximation algorithm for k ≥ 3.
3223686
Personalized Knowledge Discovery: Mining Novel Association Rules from Text
This paper presents a methodology for personalized knowledge discovery from text. It derives a user’s background knowledge from his/her background documents, and exploits such knowledge to evaluate the novelty of discovered knowledge in the form of association rules by measuring the semantic distance between the antecedent and the consequent of a rule in the background knowledge. The experiment results show that the proposed user-oriented novelty measure is highly correlated with the human subjective rule novelty and usefulness ratings. It outperforms seven major objective interestingness measures and the WordNet novelty measure for identifying novel and useful rules.
15165209
A PCA-Based Vehicle Classification Framework
Due to its great practical importance, Intelligent Transportation System has been an active research area in recent years. In this paper, we present a framework that incorporates various aspects of an intelligent transportation system with its ultimate goal being vehicle classification. Given a traffic video sequence, the proposed system first proceeds to segment individual vehicles. Then the extracted vehicle objects are normalized so that all vehicles are aligned along the same direction and measured at the same scale. Following the preprocessing step, two classification algorithms - Eigenvehicle and PCA-SVM, are proposed and implemented to classify vehicle objects into trucks, passenger cars, vans, and pick-ups. These two methods exploit the distinguishing power of Principal Component Analysis (PCA) at different granularities with different learning mechanisms. Experiments are conducted to compare these two methods and the results demonstrate the effectiveness of the proposed framework.
12374753
Efficient Method for Geometric Attribute Estimation
A new method is proposed to estimate the discrete geometric attributes, such as normal vector and curvature. The method contains two steps: points sampling and 4D Shepard interpolation. In the first step, we extract points from point cloud and estimate the attribute of the sampled points. During the second step, we interpolate a global 4D Shepard surface over these sampled points and compute the attribute of each point in point cloud. This method is very effective for using a new global surface model. It benefits many applications such as segmentation, feature extraction, data preprocessing and etc. Application examples are given to verify the proposed method.
2587881
Isomap Based on the Image Euclidean Distance
Scientists find that the human perception is based on the similarity on the manifold of data set. Isometric feature mapping (isomap) is one of the representative techniques of manifold. It is intuitive, well understood and produces reasonable mapping results. However, if the input data for manifold learning are corrupted with noises, the isomap algorithm is topologically unstable. In this paper, we present an improved manifold learning method when the input data are images - the image Euclidean distance based isomap (imisomap), in which we use a new distance for images called image Euclidean distance (IMED). Experimental results demonstrate a consistent performance improvement of the algorithm imisomap over the traditional isomap based on Euclidean distance