6087177
Design-specific path delay testing in lookup-table-based FPGAs
Due to the increased use of field-programmable gate arrays (FPGAs) in production circuits with high reliability requirements, the design-specific testing of FPGAs has become an important topic for research. Path delay testing of FPGAs is especially important since path delay faults can render an otherwise fault-free FPGA unusable for a given design layout. This paper presents a new approach for FPGA path delay testing, which partitions target paths into subsets that are tested in the same test configuration. Each path is tested for all combinations of signal inversions along the path length. Each configuration consists of a sequence generator, response analyzer, and circuitry for controlling inversions along tested paths, all of which are formed from FPGA resources not currently under test. Two algorithms are presented for target-path partitioning to determine the number of required test configurations. The test circuitry associated with these methods is also described. The results of applying the methods indicate that our path-delay-testing approach requires seconds per design to cover all paths with delay within 10% of the critical path delay. The approach has been validated using Xilinx Virtex devices.
18802067
Adaptive fault recovery for networked reconfigurable systems
The device-level size and complexity of reconfigurable architectures makes fault tolerance an important concern in system design. In this paper, we introduce a fully automated fault recovery system for networked systems, which contain FPGAs (field programmable gate arrays). If a fault is detected hat cannot be addressed locally, fault information is transferred to a reconfiguration server. Following design recompilation to avoid the fault, a new FPGA configuration is returned to the remote system and computation is reinitiated. To illustrate the benefit of this approach, we have implemented a complete fault recovery system, which requires no manual intervention. An important part of the system is a timing-driven incremental router for Xilinx Virtex devices. This router is directly interfaced to Xilinx JBits and uses no CAD tools from the standard Xilinx Alliance tool flow. Our completed system has been applied to three benchmark designs and exhibits complete fault recovery in up to 12x less time than the standard incremental Xilinx PAR flow.
16849942
Tetris: a new register pressure control technique for VLIW processors
The run-time performance of VLIW (very long instruction word) microprocessors depends heavily on the effectiveness of its associated optimizing compiler. Typical VLIW compiler phases include instruction scheduling, which maximizes instruction level parallelism (ILP), and register allocation, which minimizes data spills to external memory. If ILP is maximized without considering register constraints, high register pressure may result, leading to increased spill code and reduced run-time performance. In this paper, a new register pressure reduction technique for embedded VLIW processors is presented to control register pressure prior to instruction scheduling and register allocation. By modifying the relative ordering of operations, this technique restructures code to better reduce spills. Our technique has been implemented in Trimaran, an academic VLIW compiler, and evaluated using a series of VLIW benchmarks. Experimental results show that, on average, our algorithm reduces dynamic spills and improves overall cycle counts by 6% for a VLIW architecture with 8 functional units and 32 registers versus previous spill code reduction techniques.
57229289
An algorithm for multiple object trajectory tracking
Most tracking algorithms are based on the maximum a posteriori (MAP) solution of a probabilistic framework called Hidden Markov Model, where the distribution of the object state at current time instance is estimated based on current and previous observations. However this approach is prone to errors caused by temporal distractions such as occlusion, background clutter and multi-object confusion. In this paper we propose a multiple object tracking algorithm that seeks the optimal state sequence which maximizes the joint state-observation probability. We name this algorithm trajectory tracking since it estimates the state sequence or "trajectory" instead of the current state. The algorithm is capable of tracking multiple objects whose number is unknown and varies during tracking. We introduce an observation model which is composed of the original image, the foreground mask given by background subtraction and the object detection map generated by an object detector The image provides the object appearance information. The foreground mask enables the likelihood computation to consider the multi-object configuration in its entirety. The detection map consists of pixel-wise object detection scores, which drives the tracking algorithm to perform joint inference on both the number of objects and their configurations efficiently.
17071534
Document clustering with prior knowledge
Document clustering is an important tool for text analysis and is used in many different applications. We propose to incorporate prior knowledge of cluster membership for document cluster analysis and develop a novel semi-supervised document clustering model. The method models a set of documents with weighted graph in which each document is represented as a vertex, and each edge connecting a pair of vertices is weighted with the similarity value of the two corresponding documents. The prior knowledge indicates pairs of documents that known to belong to the same cluster. Then, the prior knowledge is transformed into a set of constraints. The document clustering task is accomplished by finding the best cuts of the graph under the constraints. We apply the model to the Normalized Cut method to demonstrate the idea and concept. Our experimental evaluations show that the proposed document clustering model reveals remarkable performance improvements with very limited training samples, and hence is a very effective semi-supervised classification tool.
1800800
Jamming sensor networks: attack and defense strategies
Wireless sensor networks are built upon a shared medium that makes it easy for adversaries to conduct radio interference, or jamming, attacks that effectively cause a denial of service of either transmission or reception functionalities. These attacks can easily be accomplished by an adversary by either bypassing MAC-layer protocols or emitting a radio signal targeted at jamming a particular channel. In this article we survey different jamming attacks that may be employed against a sensor network. In order to cope with the problem of jamming, we discuss a two-phase strategy involving the diagnosis of the attack, followed by a suitable defense strategy. We highlight the challenges associated with detecting jamming. To cope with jamming, we propose two different but complementary approaches. One approach is to simply retreat from the interferer which may be accomplished by either spectral evasion (channel surfing) or spatial evasion (spatial retreats). The second approach aims to compete more actively with the interferer by adjusting resources, such as power levels and communication coding, to achieve communication in the presence of the jammer.
1287667
Video object segmentation by motion-based sequential feature clustering
Segmentation of video foreground objects from background has many important applications, such as human computer interaction, video compression, multimedia content editing and manipulation. Most existing methods work on image pixels or color segments which are computationally expensive. Some methods require extensive manual inputs, static cameras, and/or rigid scenes. In this paper we propose a fully automatic foreground segmentation method based on sequential clustering of sparse image features. The sparseness makes the method computationally efficient. We use both edge and corner points extracted from each video frame. A joint spatio-temporal linear regression method is developed to compute sparse motion layers of M consecutive frames jointly under the temporal consistency constraint. Once the sparse motion layers have been identified for each frame, the corresponding dense motion layers are created using the Markov Random Field (MRF) model. The MRF model assigns the rest of the image pixels to the motion layers by considering both the color attributes and the spatial relations between each pixel and its surrounding edge/corner points. Experimental evaluations on videos taken by webcams show the effectiveness of the proposed method.
18166781
A State-Based Approach to Testing Aspect-Oriented Programs
This paper presents a state-based approach to testing aspect-oriented programs. Aspectual state models, as an extension to the testable FREE state model of classes, are exploited to capture the impact of aspects on the state models of classes. To generate test suites for adequately testing object behavior and interaction between classes and aspects in terms of message sequences, we transform an aspectual state model to a transition tree, where each path from the root to some leaf node indicates a template of test cases, i.e. message sequences. Since the statebased approach is directly built upon the test design patterns for object-oriented programs, it is not only applicable to the simultaneous development of classes and aspects, but also to the incremental development of aspects based on the existing classes.