35585526
On the Design of Fault-Tolerant Two-Dimensional Systolic Arrays for Yield Enhancement
The authors propose a unified approach to the design of the fault-tolerant systolic arrays incorporating design for testability, a testing scheme, a reconfiguration algorithm, time-complexity analysis of the proposed reconfiguration algorithm, and yield analysis. A main feature of the proposed designs is that multiple processing elements in a 2-D array can be tested simultaneously, thus reducing the testing time significantly. Another feature is that with the introduction of delay registers, the proposed reconfiguration algorithm reconfigures a faulty 2-D systolic array into a fault-free array without reducing throughput. The overall aim is to provide a design for a 2-D systolic array that produces high yield in VLSI/WSI implementations. >
10981925
Geometrical Learning Algorithm for Multilayer Neural Networks in a Binary Field
A geometrical expansion learning algorithm for multilayer neural networks using unipolar binary neurons with integer connection weights, which guarantees convergence for any Boolean function, is introduced. Neurons in the hidden layer develop as necessary without supervision. In addition, the computational amount is much less than that of the backpropagation algorithm. >
38004947
On-Line Detection of Errors in Homogeneous Multiprocessor Systems
27298271
The Rule-Based Approach to Reconfiguration of 2-D Processor Arrays
The authors propose to set up assignment rules based on the available interconnection resources of 2-D processor arrays. The reconfiguration of 2-D processor arrays is guided by the assignment rules, such that logical cells are always connected through the available interconnection resources. The advantage of the proposed approach is the easy adaptation to various system requirements, by adjusting assignment rules. The proposed reconfiguration algorithm can be extended to arrays with clustered faulty cells, overcoming the weakness of fixed domain approaches. >
31990528
Diagnosing Multiple Bridge Faults in Baseline Multistage Interconnection Networks
21052607
On Easily Testable and Reconfigurable Two-Dimensional Systolic Arrays
1994375
Effective Temporal Aggregation Using Point-Based Trees
Temporal databases introduce the concept of time into underlying data, and provide built-in facilities that allow users to store and retrieve time-varying data. The aggregation in temporal databases, that is, temporal aggregation is an extension of conventional aggregation on the domain and range of aggregates to include time concept. Temporal aggregation is important for various applications, but is very expensive. In this paper, we propose a new tree structure for temporal aggregation, called PA-tree, and aggregate processing method based on the PA-tree. We show that the time complexity of the proposed method is better than those of the existing methods. The time complexity of the proposed method is shown to be indeed the lower bound of the problem. We perform comparative experiments and show the performance advantage of our proposed method in practice.
6738575
Agent-oriented software modeling
Due to the increased applications of agents, agent-oriented software becomes large and complex. To support systematic developments of such software, the agent-oriented software development methodology needs to be developed. This paper focuses on the modeling phase of agent-oriented software development. For agent-oriented software modeling, the Agent Elicitation method, Intra and Inter Agent modeling method are proposed. Agent Elicitation shows how to extract agents from classes in the real world. Modeling methods of agent's characteristics-Goal, Belief, Plan and Capability-are proposed for Intra Agent modeling. Methods of agent's mobility and communication in multiagent systems are proposed for Inter Agent modeling.
12688706
Unsupervised Learning from Complex Data: The Matrix Incision Tree Algorithm
Analysis of large-scale gene expression data requires novel methods for knowledge discovery and predictive model building as well as clustering. Organizing data into meaningful structures is one of the most fundamental modes of learning. DNA microarray data set can be viewed as a set of mutually associated genes in a high-dimensional space. This paper describes a novel method to organize a complex high-dimensional space into successive lower-dimensional spaces based on the geometric properties of the data structure in the absence of a priori knowledge. The matrix incision tree algorithm reveals the hierarchical structural organization of observed data by determining the successive hyperplanes that 'optimally' separate the data hyperspace. The algorithm was tested against published data sets yielding promising results.
26955217
On Temporal Aggregate Processing Based on Time Points
Temporal aggregation is an extension of conventional aggregate on the domain and range of aggregates to include the time concept. In this paper, we propose a new tree-based temporal aggregation processing method. The time complexity of the proposed method is better than those of the existing methods. The experimental results indicate that our proposed method ourperforms the existing methods.
7701199
Gradient LASSO for feature selection
LASSO (Least Absolute Shrinkage and Selection Operator) is a useful tool to achieve the shrinkage and variable selection simultaneously. Since LASSO uses the L1 penalty, the optimization should rely on the quadratic program (QP) or general non-linear program which is known to be computational intensive. In this paper, we propose a gradient descent algorithm for LASSO. Even though the final result is slightly less accurate, the proposed algorithm is computationally simpler than QP or non-linear program, and so can be applied to large size problems. We provide the convergence rate of the algorithm, and illustrate it with simulated models as well as real data sets.