Facilitating design learning through faceted classification of in-service information

The maintenance and service records collected and maintained by engineering companies are a useful resource for the ongoing support of products. Such records are typically semi-structured and contain key information such as a description of the issue and the product affected. It is suggested that further value can be realised from the collection of these records for indicating recurrent and systemic issues which may not have been apparent previously. This paper presents a faceted classification approach to organise the information collection that might enhance retrieval and also facilitate learning from in-service experiences. The faceted classification may help to expedite responses to urgent in-service issues as well as to allow for patterns and trends in the records to be analysed, either automatically using suitable data mining algorithms or by manually browsing the classification tree. The paper describes the application of the approach to aerospace in-service records, where the potential for knowledge discovery is demonstrated

SLS-PLAN-IT: A knowledge-based blackboard scheduling system for Spacelab life sciences missions

The primary scheduling tool in use during the Spacelab Life Science (SLS-1) planning phase was the operations research (OR) based, tabular form Experiment Scheduling System (ESS) developed by NASA Marshall. PLAN-IT is an artificial intelligence based interactive graphic timeline editor for ESS developed by JPL. The PLAN-IT software was enhanced for use in the scheduling of Spacelab experiments to support the SLS missions. The enhanced software SLS-PLAN-IT System was used to support the real-time reactive scheduling task during the SLS-1 mission. SLS-PLAN-IT is a frame-based blackboard scheduling shell which, from scheduling input, creates resource-requiring event duration objects and resource-usage duration objects. The blackboard structure is to keep track of the effects of event duration objects on the resource usage objects. Various scheduling heuristics are coded in procedural form and can be invoked any time at the user's request. The system architecture is described along with what has been learned with the SLS-PLAN-IT project

Contextual Bandit Modeling for Dynamic Runtime Control in Computer Systems

Modern operating systems and microarchitectures provide a myriad of mechanisms for monitoring and affecting system operation and resource utilization at runtime. Dynamic runtime control of these mechanisms can tailor system operation to the characteristics and behavior of the current workload, resulting in improved performance. However, developing effective models for system control can be challenging. Existing methods often require extensive manual effort, computation time, and domain knowledge to identify relevant low-level performance metrics, relate low-level performance metrics and high-level control decisions to workload performance, and to evaluate the resulting control models. This dissertation develops a general framework, based on the contextual bandit, for describing and learning effective models for runtime system control. Random profiling is used to characterize the relationship between workload behavior, system configuration, and performance. The framework is evaluated in the context of two applications of progressive complexity; first, the selection of paging modes (Shadow Paging, Hardware-Assisted Page) in the Xen virtual machine memory manager; second, the utilization of hardware memory prefetching for multi-core, multi-tenant workloads with cross-core contention for shared memory resources, such as the last-level cache and memory bandwidth. The resulting models for both applications are competitive in comparison to existing runtime control approaches. For paging mode selection, the resulting model provides equivalent performance to the state of the art while substantially reducing the computation requirements of profiling. For hardware memory prefetcher utilization, the resulting models are the first to provide dynamic control for hardware prefetchers using workload statistics. Finally, a correlation-based feature selection method is evaluated for identifying relevant low-level performance metrics related to hardware memory prefetching

Genetic Algorithms and Their Application to the Protein Folding Problem

The protein folding problem involves the prediction of the secondary and tertiary structure of a molecule given the primary structure. The primary structure defines sequence of amino-acid residues, while the secondary structure describes the local 3-dimensional arrangement of amino-acid residues within the molecule. The relative orientation of the secondary structural motifs, namely the tertiary structure, defines the shape of the entire biomolecule. The exact, mechanism by which a sequence of amino acids protein folds into its 3- dimensional conformation is unknown Current approaches to the protein folding problem include calculus-based methods, systematic search, model building and symbolic methods, random methods such as Monte Carlo simulation and simulated annealing, distance geometry, and molecular dynamics. Many of these current approaches search for conformations which minimize the internal energy of the molecule. A genetic algorithm GA, a stochastic search technique modeled after natural adaptive systems, potentially offers significant speedup over other search algorithms because of its inherent parallelizability. The results of applying a parallel GA to the protein folding problem show significant improvement in execution time when compared to serial implementations of the GA. In addition, the parallel GA demonstrates good scalability characteristics since the communications strategy used to manage the population can be tailored to the parallel architecture

A Literature Review On Combining Heuristics and Exact Algorithms in Combinatorial Optimization

There are several approaches for solving hard optimization problems. Mathematical programming techniques such as (integer) linear programming-based methods and metaheuristic approaches are two extremely effective streams for combinatorial problems. Different research streams, more or less in isolation from one another, created these two. Only several years ago, many scholars noticed the advantages and enormous potential of building hybrids of combining mathematical programming methodologies and metaheuristics. In reality, many problems can be solved much better by exploiting synergies between these approaches than by “pure” classical algorithms. The key question is how to integrate mathematical programming methods and metaheuristics to achieve such benefits. This paper reviews existing techniques for such combinations and provides examples of using them for vehicle routing problems

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context, reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated in the existing deep learning ecosystem to provide a tunable balance between performance, power consumption and programmability. In this paper, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a comparative study of their key characteristics which include the supported applications, architectural choices, design space exploration methods and achieved performance. Moreover, major challenges and objectives introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform evaluation methodology is proposed, aiming at the comprehensive, complete and in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal, 201