34798470
Optimal Design and Sequential Analysis of VLSI Testing Strategy
A method for determining the optimal testing period and measuring the production yield is discussed. With the increased complexity of VLSI circuits, testing has become more costly and time-consuming. The design of a testing strategy, which is specified by the testing period based on the coverage function of the testing algorithm, involves trading off the cost of testing and the penalty of passing a bad chip as good. The optimal testing period is first derived, assuming the production yield is known. Since the yield may not be known a priori, an optimal sequential testing strategy which estimates the yield based on ongoing testing results, which in turn determines the optimal testing period, is developed next. Finally, the optimal sequential testing strategy for batches in which N chips are tested simultaneously is presented. The results are of use whether the yield stays constant or varies from one manufacturing run to another. >
920212
Resource scheduling in dependable integrated modular avionics
In the recent development of avionics systems, integrated modular avionics (IMA) is advocated for next generation architecture that needs integration of mixed criticality real-time applications. These integrated applications meet their own timing constraints while sharing avionics computer resources. To guarantee timing constraints and dependability of each application, an IMA-based system is equipped with the schemes for spatial and temporal partitioning. We refer the model as SP-RTS (strongly partitioned real-time system), which deals with processor partitions and communication channels as its basic scheduling entities. This paper presents a partition and channel-scheduling algorithm for the SP-RTS. The basic idea of the algorithm is to use a two-level hierarchical schedule that activates partitions (or channels) following a distance-constraints guaranteed cyclic schedule and then dispatches tasks (or messages) according to a fixed priority schedule. To enhance schedulability, we devised heuristic algorithms for deadline decomposition and channel combining. The simulation results show the schedulability analysis of the two-level scheduling algorithm and the beneficial characteristics of the proposed deadline decomposition and channel combining algorithms.
10753893
Evaluation of Error Recovery Blocks Used for Cooperating Processes
Three alternatives for implementing recovery blocks (RB's) are conceivable for backward error recovery in concurrent processing. These are the asynchronous, synchronous, and the pseudorecovery point implementations. Asynchronous RB's are based on the concept of maximum autonomy in each of concurrent processes. Consequently, establishment of RB's in a process is made independently of others and unbounded rollback propagations become a serious problem. In order to completely avoid unbounded rollback propagations, it is necessary to synchronize the establishment of recovery blocks in all cooperating processes. Process autonomy is sacrificed and processes are forced to wait for commitments from others to establish a recovery line, leading to inefficiency in time utilization. As a compromise between asynchronous and synchronous RB's we propose to insert pseudorecovery points (PRP's) so that unbounded rollback propagations may be avoided while maintaining process autonomy. We developed probabilistic models for analyzing these three methods under standard assumptions in computer performance analysis, i.e., exponential distributions for related random variables. With these models we have estimated 1) the interval between two successive recovery lines for asynchronous RB's, 2) mean loss in computation power for the synchronized method, and 3) additional overhead and rollback distance in case PRP's are used.
40618061
Transaction Recovery in Distributed DB/DC Systems: A Progressive Approach
31591898
Scanning the issue - special issue on real-time systems
30590203
Efficient State-Saving Architectures for Power-Mode Switching
Time and energy is expended in switching between power modes (e.g., active, hibernate, sleep, etc.). Powering off cache is one major reason for this. When there is a switch in the power-mode involving cache power-off, the system spends time and energy in filling the cache with new data (inherent cache misses). In our technique, before powering off the cache, we save its state in Embedded DRAM and bring it back when the previous power mode is restored. Our experiments have showed that in a majority of cases the cache contents are too valuable to be erased. By saving the contents we can reduce switching speed and energy. We present a heuristic to save the most relevant cache contents so that power and delay overheads are minimized. To measure the area overhead a synthesizable VHDL model was designed.
14604625
Single run optimization using the reverse-simulation method
An efticient “Simulation Optimization” technique is developed to solve system design problems which can not be expressed in explicit analytical or mathematical models. In particular, we explore a new paradigm called the “Reverse-Simulation optimization method” which is quite Merent from current simulation optimization methods in the literature. This paper focuses on the method of on-line determination of steady-state, which is a very important issue in ReverseSimulation optimization, and the construction of a Reverse-Simulation algorithm with expert systems. The proposed algorithm finds the steady-state of a system and an optimal state. The algorithm employs the Lyapunov exponent of Chaos theory to determine both the steady-state and optimal state of a system.
8697700
Probabilistic Retrieval Incorporating the Relationships of Descriptors Incrementally
The previous probabilistic retrieval models assume that the relevance probability of a document is independent of the descriptors that are not specified in a query. This is not true in practice because there can be many descriptors that represent the same concept. The probabilistic retrieval model developed in this paper overcomes this unsuitable assumption and incorporates the relationships of descriptors. A learning method is also proposed to figure out the relationships incrementally. Each time retrieval results are available, the method identifies in the relevant documents the descriptors that designate the concepts specified by the query descriptors. Although it employs user feedbacks like relevance feedback, it attempts to capture certain stable relationships of descriptors from many past user queries rather than to distinguish relevant documents from non-relevant ones for a particular query. We show through experiments that the proposed information retrieval method improves retrieval effectiveness over time.
46434197
Flow Control Strategy for Flexible Machining and Assembly System
Abstract In this paper an effective flow control strategy for a Flexible Machining and Assembly System is proposed using the concepts of operation-oriented and, combined push and pull control method. The proposed flow control mechanism could meet production demands with a minimum makespan while satisfying assigned due-dates and keeping a low volume of work-in-process at the same time. It also considers machine failures and rush jobs.
14282973
Closing the Gap: Learning-Based Information Extraction Rivaling Knowledge-Engineering Methods
In this paper, we present a learning approach to the scenario template task of information extraction, where information filling one template could come from multiple sentences. When tested on the MUC-4 task, our learning approach achieves accuracy competitive to the best of the MUC-4 systems, which were all built with manually engineered rules. Our analysis reveals that our use of full parsing and state-of-the-art learning algorithms have contributed to the good performance. To our knowledge, this is the first research to have demonstrated that a learning approach to the full-scale information extraction task could achieve performance rivaling that of the knowledge engineering approach.
404374
The Learning and Emergence of Mildly Context Sensitive Languages
This paper describes a framework for studies of the adaptive acquisition and evolution of language, with the following components: language learning begins by associating words with cognitively salient representations (“grounding”); the sentences of each language are determined by properties of lexical items, and so only these need to be transmitted by learning; the learnable languages allow multiple agreements, multiple crossing agreements, and reduplication, as mildly context sensitive and human languages do; infinitely many different languages are learnable; many of the learnable languages include infinitely many sentences; in each language, inferential processes can be defined over succinct representations of the derivations themselves; the languages can be extended by innovative responses to communicative demands. Preliminary analytic results and a robotic implementation are described.