18427300
Exploring Functional Unit Design Space of VLIW Processors for Optimizing Both Performance and Energy Consumption
The number of functional units can have significant impact on both the performance and energy consumption of VLIW processors. This paper uses a design exploration approach to find optimal integer functional unit configurations for achieving the best EDP (energy delay product) results for different media applications. Our experimental results quantitatively indicate that the optimal number of integer functional units should match the instruction level parallelism that can be extracted from the applications to balance both performance and energy optimally for VLIW processors.
9354212
High-Performance Job-Shop Scheduling With A Time-Delay TD-lambda Network
Job-shop scheduling is an important task for manufacturing industries. We are interested in the particular task of scheduling payload processing for NASA's space shuttle program. This paper summarizes our previous work on formulating this task for solution by the reinforcement learning algorithm T D(). A shortcoming of this previous work was its reliance on hand-engineered input features. This paper shows how to extend the time-delay neural network (TDNN) architecture to apply it to irregular-length schedules. Experimental tests show that this TDNN-TD() network can match the performance of our previous hand-engineered system. The tests also show that both neural network approaches signiicantly out-perform the best previous (non-learning) solution to this problem in terms of the quality of the resulting schedules and the number of search steps required to construct them.
43025995
Processing Probabilistic Range Query over Imprecise Data Based on Quality of Result
Sensors are employed in many applications to monitor entities such as environment temperature or illumination. Because of limited resources (e.g., battery power and wireless bandwidth), it is impractical for the database to track continuous changing readings from sensors at all times. Consequently, database may produce incorrect query results based on recorded data. However, if the error between the recorded value and the actual value is bounded, probabilistic approach can be applied to process queries over the imprecise data in database. The paper proposes a quality of result based probabilistic range query processing method. Two measures are defined to evaluate the quality of result. A probability model is proposed to estimate the quality of returned results. Based on the model, an algorithm is developed to choose the probability threshold according to user’s requirement on the quality of result.
17633305
The Impact of Cache Organization in Optimizing Microprocessor Power Consumption
In the recent years, power consumption has become increasingly an important design concern as silicon area and performance in modern computer systems design. Several factors have contributed to this trend. Perhaps the most visible have been the remarkable success and growth of Personal Digital Assistants (PDA’s), Cellular phones and pagers, etc .This work is an attempt to explore the impact of feature size shrinkage and cache configuration on optimizing power consumption in modern processors. The work studies two commercially known RISC microprocessors. The StrongARM and the Alpha-21064 microprocessors. The latter is the ancestor of the former and is therefore used here as the baseline in our analysis. Our analysis has illustrated quantitatively great power saving when technology is downsized and cache is well organized
14422830
Compiler-guided register reliability improvement against soft errors
With the scaling of technology, transient errors caused by external particle strikes have become a critical challenge for microprocessor design. As embedded processors are widely used in reliability-sensitive environments, it becomes increasingly important to develop cost-effective techniques to improve the processor reliability against soft errors. This paper focuses on studying the register file immunity against soft errors since modern processors typically employ a large number of registers, which are accessed very frequently. As a result, soft errors occurred in registers can easily propagate to functional units or the memory system, leading to silent data error (SDC) or system crash.To develop cost-effective techniques to fight soft errors for embedded processors, the first step is to understand the register file susceptibility to soft errors and its impact on the system reliability accurately. Toward this goal, this paper proposes the concept of register vulnerability factor (RVF) to characterize the probability that register transient errors can escape the register file and thus potentially impact the system reliability. Built upon the RVF concept, we then propose two cost-effective compiler-guided techniques to improve the register file reliability by lowering the RVF value. Our experiments indicate that on average, the RVF can be reduced to 9.1% and 9.5% by the hyperblock-based instruction re-scheduling and the reliability-oriented register assignment respectively, which can potentially lower the reliability cost significantly while protecting register files against transient errors.
12895493
Improvement of HITS-based algorithms on web documents
In this paper, we present two ways to improve the precision of HITS-based algorithms on Web documents. First, by analyzing the limitations of current HITS-based algorithms, we propose a new weighted HITS-based method that assigns appropriate weights to in-links of root documents. Then, we combine content analysis with HITS-based algorithms and study the effects of four representative relevance scoring methods, VSM, Okapi, TLS, and CDR, using a set of broad topic queries. Our experimental results show that our weighted HITS-based method performs significantly better than Bharat's improved HITS algorithm. When we combine our weighted HITS-based method or Bharat's HITS algorithm with any of the four relevance scoring methods, the combined methods are only marginally better than our weighted HITS-based method. Between the four relevance-scoring methods, there is no significant quality difference when they are combined with a HITS-based algorithm.
8016353
Restricted Value Iteration: Theory and Algorithms
Value iteration is a popular algorithm for finding near optimal policies for POMDPs. It is ineffcient due to the need to account for the entire belief space, which necessitates the solution of large numbers of linear programs. In this paper, we study value iteration restricted to belief subsets. We show that, together with properly chosen belief subsets, restricted value iteration yields near-optimal policies and we give a condition for determining whether a given belief subset would bring about savings in space and time. We also apply restricted value iteration to two interesting classes of POMDPs, namely informative POMDPs and near-discernible POMDPs.
18395713
Value Iteration over Belief Subspace
Partially Observable Markov Decision Processes (POMDPs) provide an elegant framework for AI planning tasks with uncertainties. Value iteration is a well-known algorithm for solving POMDPs. It is notoriously difficult because at each step it needs to account for every belief state in a continuous space. In this paper, we show that value iteration can be conducted over a subset of belief space. Then, we study a class of POMDPs, namely informative POMDPs, where each observation provides good albeit incomplete information about world states. For informative POMDPs, value iteration can be conducted over a small subset of belief space. This yields two advantages: First, fewer vectors are in need to represent value functions. Second, value iteration can be accelerated. Empirical studies are presented to demonstrate these two advantages.
8019143
An average-case analysis of graph search
Many problems in real-world applications require searching graphs. Understanding the performance of search algorithms has been one of the eminent tasks of heuristic search research. Despite the importance of graph search algorithms, the research of analyzing their performance is limited, and most work on search algorithm analysis has been focused on tree search algorithms. One of the major obstacles to analyzing graph search is that no single graph is an appropriate representative of graph search problems. In this paper, we propose one possible approach to analyzing graph search: Analyzing the performance of graph search algorithms on a representative graph of a cluster of problems. We specifically consider job-sequencing problems in which a set of jobs must be sequenced on a machine such that a penalty function is minimized. We analyze the performance of A* graph search algorithm on an abstract model that closely represents job sequencing problems. It is an extension to a model widely used previously for analyzing tree search. One of the main results of our analysis is the existence of a gap of computational cost between two classes of job sequencing problems, one with exponential and the other with polynomial complexity. We provide experimental results showing that real job sequencing problems indeed have a huge difference on computational costs under different conditions.
12967582
An Improved Integer Local Search for Complex Scheduling Problems
We consider complex scheduling problems that can be captured as optimization under hard and soft constraints. The objective of such an optimization problem is to satisfy as many hard constraints as possible and meanwhile to minimize a penalty function determined by the unsatisfied soft constraints. We present an efficient local search algorithm for these problems which improves upon Wsat(oip), a Walksat-based local search algorithm for overconstrained problems represented in integer programs. We introduce three techniques to the Wsat(oip) algorithm to extend its capability and improve its performance: backbone guided biased moves to drive the search to the regions in search space where high-quality and optimal solutions reside; sampling-based aspiration search to reduce search cost and make anytime solutions available over the course of the search; and dynamic parameter tuning to dynamically adjust the key parameters of the algorithm to make it robust and flexible for various applications. Our experimental results on large-scale crew scheduling, basketball tournament scheduling and progressive party scheduling show that the new improved algorithm can find better solutions with less computation than Wsat(oip).
7272422
Identification and Evaluation of Weak Community Structures in Networks
Identifying intrinsic structures in large networks is a fundamental problem in many fields, such as engineering, social science and biology. In this paper, we are concerned with communities, which are densely connected sub-graphs in a network, and address two critical issues for finding community structures from large experimental data. First, most existing network clustering methods assume sparse networks and networks with strong community structures. In contrast, we consider sparse and dense networks with weak community structures. We introduce a set of simple operations that capture local neighborhood information of a node to identify weak communities. Second, we consider the issue of automatically determining the most appropriate number of communities, a crucial problem for all clustering methods. This requires to properly evaluate the quality of community structures. Built atop a function for network cluster evaluation by Newman and Girvan, we extend their work to weighted graphs. We have evaluated our methods on many networks of known structures, and applied them to analyze a collaboration network and a genetic network. The results showed that our methods can find superb community structures and correct numbers of communities. Comparing to the existing approaches, our methods performed significantly better on networks with weak community structures and equally well on networks with strong community structures.