9192961
Counterexample-Guided Abstraction Refinement
The main practical problem in model checking is the combinatorial explosion of system states commonly known as the state explosion problem. Abstraction methods attempt to reduce the size of the state space by employing knowledge about the system and the specification in order to model only relevant features in the Kripke structure. Counterexample-guided abstraction refinement is an automatic abstraction method where, starting with a relatively small skeletal representation of the system to be verified, increasingly precise abstract representations of the system are computed. The key step is to extract information from false negatives ("spurious counterexamples") due to over-approximation.
7665820
Equivalence checking using abstract BDDs
We introduce a new equivalence checking method based on abstract BDDs (aBDDs). The basic idea is the following: given an abstraction function, aBDDs reduce the size of BDDs by merging nodes that have the same abstract value. An aBDD has bounded size and can be constructed without constructing the original BDD. We show that this method of equivalence checking is always sound. It is complete for an important class of arithmetic circuits that includes integer multiplication. We also suggest heuristics for findings suitable abstraction functions based on the structure of the circuit. The efficiency of this technique is illustrated by experiments on ISCAS'85 benchmark circuits.
6121809
Tree-like counterexamples in model checking
Counter examples for specification violations provide engineers with important debugging information. Although counterexamples are considered one of the main advantages of model checking, state-of the art model checkers are restricted to relatively simple counterexamples, and surprisingly little research effort has been put into counterexamples. In this paper, we introduce a new general framework for counterexamples. The paper has three main contributions: (i) We determine the general form of ACTL counterexamples. To this end, we investigate the notion of counterexample and show that a large class of temporal logics beyond ACTL admits counterexamples with a simple tree-like transition relation. We show that the existence of tree-like counterexamples is related to a universal fragment of extended branching time logic based on w-regular temporal operators. (ii) We present new symbolic algorithms to generate tree-like counterexamples for ACTL specifications. (iii) Based on tree-like counterexamples we extend the abstraction refinement methodology developed recently by Clarke et al. (CAV'2000) to full ACTL. This demonstrates the conceptual simplicity and elegance of tree-like counterexamples.
42676253
Synchronization of large sequential circuits by partial reset
In this paper, we present a technique to synchronize large synchronous sequential circuits, which uses partial reset to break feedback loops in the circuits such that they become synchronizable. The method also provides a synchronizing sequence. Experimental results for ISCAS'89 benchmark circuits are presented, including the largest circuits, which were not synchronized previously.
158009
Threshold optimization algorithm for weak signal in distributed-sensor fusion system
Distributed order statistics constant false alarm rate (OS-CFAR) detection techniques are important for nonstationary observation with varying weak narrowband random signals. However, it is very difficult to choose system parameters to obtain optimal threshold values at the fusion center because of the nonlinear property of distributed OS-CFAR detection system. This paper provides a novel solution based on an effective and flexible genetic algorithm. Using this approach, all system parameters are directly coded in decimal chromosomes and they can be optimized simultaneously. Furthermore, we also derive the more general model by implementing the K fusion rules. The simulation results show that applying the proposed approach one can achieve comparable performances with the reported methods and results.
7140152
Energy Reduction by Workload Adaptation in a Multi-Process Environment
Reducing energy consumption is an important issue in modern computers. Dynamic power management (DPM) has been extensively studied in recent years. One approach for DPM is to adjust workloads, such as clustering or eliminating requests, as a way to trade-off energy consumption and quality of services. Previous studies focus on single processes. However, when multiple concurrently running processes are considered, workload adjustment must be determined based on the interleaving of the processes' requests. When multiple processes share the same hardware component, adjusting one process may not save energy. This paper presents an approach to assign energy responsibility to individual processes based on how they affect power management. The assignment is used to estimate potential energy reduction by adjusting the processes. We use the estimation to guide runtime adaptation of workload behavior. Experiments demonstrate that our approach can save more energy and improve energy efficiency
5344006
Min/max on-chip inductance models and delay metrics
This paper proposes an analytical inductance extraction model for characterizing min/max values of typical on-chip global intercon-nect structures, and a corresponding delay metric that can be used to provide RLC delay prediction from physical geometries. The model extraction and analysis is efficient enough to be used within optimization and physical design exploration loops. The analytical min/max inductance approximations also provide insight into the effects caused by inductances.
15882697
Coupling feature selection and machine learning methods for navigational query identification
It is important yet hard to identify navigational queries in Web search due to a lack of sufficient information in Web queries, which are typically very short. In this paper we study several machine learning methods, including naive Bayes model, maximum entropy model, support vector machine (SVM), and stochastic gradient boosting tree (SGBT), for navigational query identification in Web search. To boost the performance of these machine techniques, we exploit several feature selection methods and propose coupling feature selection with classification approaches to achieve the best performance. Different from most prior work that uses a small number of features, in this paper, we study the problem of identifying navigational queries with thousands of available features, extracted from major commercial search engine results, Web search user click data, query log, and the whole Web's relational content. A multi-level feature extraction system is constructed.Our results on real search data show that 1) Among all the features we tested, user click distribution features are the most important set of features for identifying navigational queries. 2) In order to achieve good performance, machine learning approaches have to be coupled with good feature selection methods. We find that gradient boosting tree, coupled with linear SVM feature selection is most effective. 3) With carefully coupled feature selection and classification approaches, navigational queries can be accurately identified with 88.1% F1 score, which is 33% error rate reduction compared to the best uncoupled system, and 40% error rate reduction compared to a well tuned system without feature selection.
702707
Rate-distortion analysis for H.264/AVC video coding and its application to rate control
In this paper, an efficient rate-control scheme for H.264/AVC video encoding is proposed. The redesign of the quantization scheme in H.264/AVC results in that the relationship between the quantization parameter and the true quantization stepsize is no longer linear. Based on this observation, we propose a new rate-distortion (R-D) model by utilizing the true quantization stepsize and then develop an improved rate-control scheme for the H.264/AVC encoder based on this new R-D model. In general, the current R-D optimization (RDO) mode-selection scheme in H.264/AVC test model is difficult for rate control, because rate control usually requires a predetermined set of motion vectors and coding modes to select the quantization parameter, whereas the RDO does in the different order and requires a predetermined quantization parameter to select motion vectors and coding modes. To tackle this problem, we develop a complexity-adjustable rate-control scheme based on the proposed R-D model. Briefly, the proposed scheme is a one-pass process at frame level and a partial two-pass process at macroblock level. Since the number of macroblocks with the two-pass processing can be controlled by an encoder parameter, the fully one-pass implementation is a subset of the proposed algorithm. An additional topic discussed in this paper is about video buffering. Since a hypothetical reference decoder (HRD) has been defined in H.264/AVC to guarantee that the buffers never overflow or underflow, the more accurate rate-allocation schemes are proposed to satisfy these requirements of HRD.