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
28591822
Multicast Routing with Minimum Energy Cost and Minimum Delay in Wireless Sensor Networks
In this paper, we consider the multicast routing with minimum energy cost and minimum delay (MEMD) in wireless sensor networks under the access control scheme of Spatial Time Division Multiple Access (STDMA). We formulate and explore both MEMD multicast and ME multicast, and show that the latter is just the Maximum Leaf Spanning Tree (MLST) problem, which is NP-complete. A 2-approximation algorithm is proposed for the MLST problem through improving a known one. Based on this algorithm, an approximation algorithm is obtained for the MEMD multicast problem. To further improve the delay result, we provide another approximation algorithm for our main problem using a different approach. These algorithms are all near linear in the size of the network graph, and also are shown to have good performance by simulation results.
14318300
Features for neural net based region identification of newspaper documents
Several features for neural network based document region identification are tested. Specifically, this paper examines features for non-text region identification. The neural network based region identification algorithm is a key component of a document recognition system that segments a document into regions, classifies them into text, graphic, photo, and other region types, and then uses this classification to guide the processing and analysis of the image. The input data are unusually challenging: low quality images of newspaper documents obtained from microfilmed archives. The results compare favorably with other results reported in the literature.
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.
32644316
A feature oriented approach to modeling and reusing requirements of software product lines
Getting a proper set of reusable requirements is an important milestone for successful software product line (SPL) practice. But modeling SPL requirements is usually more complex and difficult than modeling requirements for individual applications because it often involves systematically exploring commonality and variation across a set of applications. This paper presents a feature-oriented approach to modeling and reusing SPL requirements. A framework of the feature model is first proposed from five aspects, namely, basic structure, variation representation mechanism, variation binding time, variation constraint mechanism and quality feature analysis. Then, a customization-based reusing method is suggested, and a feature-oriented domain modeling method (FODM) is presented, including a concrete form of the feature model and a modeling process for it. At the end, a case study of a real domain is used to validate the feature model framework and demonstrate FODM.
14855965
Detecting VLIW Hard Errors Cost-Effectively through a Software-Based Approach
Research indicates that as technology scales, hard errors such as wear-out errors are increasingly becoming a critical challenge for microprocessor design. While hard errors in memory structures can be efficiently detected by error correction code, detecting hard errors for functional units cost-effectively is a challenging problem. In this paper, we propose to exploit the idle cycles of the under-utilized VLIW functional units to run test instructions for detecting wear-out errors without increasing the hardware cost or significantly impacting performance. We also explore the design space of this software-based approach to balance the error detection latency and the performance for VLIW architectures. Our experimental results indicate that such a software-based approach can effectively detect hard errors with minimum impact on performance for VLIW processors, which is particularly useful for reliable embedded applications with cost constraints.
76909
Heuristic search in artificial intelligence
In February of this year, Claude E. Shannon and Herbert A. Simon passed away, both at age 84. In his 1950 paper entitled, “Programming a Computer to Play Chess” [3], Shannon laid out the theory of minimax search with bounded lookahead and static heuristic evaluation, the basis of almost all subsequent work on two-player games. Along with Allen Newell, Herbert Simon established the idea of heuristic search in a problem space as one of the fundamental paradigms of problem solving in artificial intelligence [1]. We feel a deep sorrow for the loss of these two giants of our field. Both men are also known for their deep scientific contributions to other fields as well. Shannon was the father of information and communication theory, and Simon was a founding father of organizational behavior and the information processing model of cognitive psychology. Although Simon and Shannon are no longer with us, their work continues as a source of inspiration in virtually every field they touched. We dedicate this special issue on heuristic search in artificial intelligence to Claude Shannon and Herbert Simon. Heuristic search remains as a core area of artificial intelligence (AI). The use of a good search algorithm is often a critical factor in the performance of an intelligent system. As with most areas of AI, there has been steady progress in heuristic search research over the years. This progress can be measured by several different yardsticks, including finding optimal solutions to larger problems, making higher quality decisions in fixedsize problems, handling more complex domains including dynamic environments with incomplete and uncertain information, being able to analyze and predict the performance of heuristic search algorithms, and the increasing deployment of real-world applications of search algorithms. The success of the Deep Blue Chess machine in defeating Gary Kasparov, the human world champion, in an exhibition match in 1997 was a landmark in the field of AI, the achievement of a goal that had been pursued for almost 50 years. By coincidence, this special issue contains the same number of papers as the Artificial Intelligence Journal special issue on Search and Heuristics, published in 1983. What Judea
3005507
Iterative state-space reduction for flexible computation
Abstract Flexible computation is a general framework for decision making under limited computational resources. It enables an agent to allocate limited computational resources to maximize its overall performance or utility. In this paper, we present a strategy for flexible computation, which we call iterative state-space reduction . The main ideas are to reduce a problem space that is difficult to search to one that is relatively easy to explore, to use the optimal solution from the reduced space as an approximate solution to the original problem, and to iteratively apply multiple reductions to progressively find better solutions. The basic operation for state-space reduction is heuristic node pruning, which excludes the nodes that do not seem to lead to high-quality solutions from further examination. Iterative state-space reduction can be combined with a state-space search, such as best-first search or depth-first branch-and-bound (DFBnB). The resulting algorithm can run as an anytime algorithm, which provides a tradeoff between solution quality and computation. Based on an analytical modal, we analyze the probability that one iteration of the iterative process finds a solution. Furthermore, by combining it within DFBnB we apply iterative state-space reduction to three combinatorial problems, the maximum Boolean satisfiability, the symmetric TSP and asymmetric TSP. Our experimental results show that iterative state-space reduction is effective and efficient, making DFBnB find better solutions with less computation.
30318957
Replication cache: a small fully associative cache to improve data cache reliability
Soft error conscious cache design has become increasingly crucial for reliable computing. The widely used ECC or parity-based integrity checking techniques have only limited capability in error detection and correction, while incurring nontrivial penalty in area or performance. The N modular redundancy (NMR) scheme is too costly for processors with stringent cost constraints. This paper proposes a cost-effective solution to enhance data reliability significantly with minimum impact on performance. The idea is to add a small fully associative cache to store the replica of every write to the L1 data cache. Due to data locality and its full associativity, the replication cache can be kept small while providing replicas for a significant fraction of read hits in L1, which can be used to enhance data integrity against soft errors. Our experiments show that a replication cache with eight blocks can provide replicas for 97.3 percent of read hits in L1 on average. Moreover, compared with the recently proposed in-cache replication schemes, the replication cache is more energy efficient, while improving the data integrity against soft errors significantly.
10248617
Towards flexible teamwork in persistent teams
Teamwork is a critical capability in multi-agent environments. Many such environments mandate that the agents and agent-teams mast be persistent i.e., exist over long periods of time. Agents in such persistent teams are bound together by their long-term common interests and goals. This paper focuses on flexible teamwork in such persistent teams. Unfortunately, while previous work has investigated flexible teamwork, persistent teams remain unexplored. For flexible teamwork, one promising approach that has emerged is model-based, i.e., providing agents with general models of teamwork that explicitly specify their commitments in teamwork. Such models enable agents to autonomously reason about coordination. Unfortunately, for persistent teams, such models may lead to coordination and communication actions that while locally optimal, are highly problematic for the team's long-term goals. We present a decision-theoretic technique to enable persistent teams to overcome such limitations of the model-based approach. In particular, agents reason about expected team utilities of future team states that are projected to result from actions recommended by the teamwork model, as well as lower-cost (or higher-cost) variations on these actions. To accommodate real-time constraints, this reasoning is done in an any-time fashion. Implemented examples from an analytic search tree and some real-world domains are presented.
10336575
Discovering accurate interclass test dependences
Knowledge of interclass test dependences is crucial to decide class test order, facilitating the design of an efficient integration test plan. In order to discover precise interclass test dependences, we proposed a semantic-based definition and designed a safe approximation algorithm to calculate the dependence according to the given definition. The algorithm propagates semantic dependences at method-level granularity and is parameterized by the precision of the corresponding program analysis. We have experimented with nine benchmarks and showed that the algorithm is rather accurate in that it discovers dependence cycles precisely in 6 out of 9 benchmarks (as evaluated by human inspection). The algorithm uncovers additional opportunities for concurrent testing in each of the benchmarks, with an on average expected 52.5% time savings over the ORD-based definition.