The concept of programmable nodes and active networks introduces programmability into communication networks. Code and data can be sent and modified on their ways to destinations. Recently, various research groups hav...
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ISBN:
(纸本)078037553X
The concept of programmable nodes and active networks introduces programmability into communication networks. Code and data can be sent and modified on their ways to destinations. Recently, various research groups have designed and implemented their own execution platforms for active networks. Each design has its own benefits and drawbacks. Hence, there exist interoperability problems among them. Therefore in this paper, we introduce a concept that is similar to the socket programming for writing network programs. A set of simple interfaces, the Active network Socket programming (ANSP), for programming active applications is established. The resulting active applications will be able to work on top of all other execution environments in future. The ANSP offers a concept that is similar to 'write once, run everywhere.' It is an openprogramming model that active applications can work on all execution environments. It solves the heterogeneity within the active networks. This is especially useful when active applications need to access all regions within a heterogeneous network to deploy special service at critical points or to monitor the performance of the entire networks. Instead of introducing a new platform, our approach provides a thin, transparent layer on top of existing environments that can be easily installed for all active applications. (C) 2003 Elsevier Science B.V. All rights reserved.
Contemporary neural architectures having one or more hidden layers suffer from the same deficiencies that genetic algorithms and methodologies for non-trivial automatic programming do;namely, they cannot exploit inher...
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ISBN:
(纸本)0780382420
Contemporary neural architectures having one or more hidden layers suffer from the same deficiencies that genetic algorithms and methodologies for non-trivial automatic programming do;namely, they cannot exploit inherent domain symmetries for the transference of knowledge from an application of lesser to greater rank, or across similar applications. As a direct consequence, no ensemble of contemporary neural architectures allows for the effective codification and transference of knowledge within a society of individuals (i.e., swarm knowledge). These deficiencies stem from the fact that contemporary neural architectures cannot reason symbolically using heuristic ontologies. They cannot directly provide symbolic explanations of what was learned for purposes of inspection and verification. Moreover, they do not allow the knowledge engineer to precondition the internal feature space through the application of domain-specific modeling languages. A symbolic representation can support the heuristic evolution of an ensemble of neural architectures. Each neural network in the ensemble imbues a hidden layer and for this reason is NP-hard in its learning performance. It may be argued that the internal use of a neat representation subsumes the heuristic evolution of a scruffy one. It follows that there is a duality of representation under transformation. The goal of Al then is to find symbolic representations, transformations, and associated heuristic ontologics. This paper provides an introduction to this quest. Consider the game of chess for example. If a neural network or symbolic heuristic is used to evaluate board positions, then the best found iterate (i.e., of weights or symbols) serves as a starting point for iterative refinement. This paper will address the ordering and similarity of the training instances in refining subsequent iterates. If we fix the learning technology, then we need to focus on reducing the problem, composing intermediate results, and transfering t
eLoom is an open source graph simulation software tool, developed at the University of New Mexico (UNM), that enables users to specify and simulate neural network models. Its specification language and libraries enabl...
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eLoom is an open source graph simulation software tool, developed at the University of New Mexico (UNM), that enables users to specify and simulate neural network models. Its specification language and libraries enables users to construct and simulate arbitrary, potentially hierarchical network structures on serial and parallel processing systems. In addition, eLoom is integrated with UNM's Flatland, an open source virtual environments development tool to provide real-time visualizations of the network structure and activity. Visualization is a useful method for understanding both learning and computation in artificial neural networks. Through 3D animated pictorially representations of the state and flow of information in the network, a better understanding of network functionality is achieved. ART-1, LAPART-II, MLP, and SOM neural networks are presented to illustrate eLoom and Mallard's capabilities. (C) 2003 Elsevier Science Ltd. All rights reserved.
programming multi-processor ASIPs, such as network processors, remains an art due to the wide variety of architectures and due to little support for exploring different implementation alternatives. We present a study ...
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ISBN:
(纸本)1581136765
programming multi-processor ASIPs, such as network processors, remains an art due to the wide variety of architectures and due to little support for exploring different implementation alternatives. We present a study that implements an IP forwarding router application on two different network processors to better understand the main challenges in programming such multi-processor ASIPs. The goal of this study is to identify the elements central to a successful deployment of such systems based on a detailed profiling of the two architectures. Our results show that inefficient partitioning can impact the throughput by more than 30%;a better arbitration of resources increases the throughput by at least 10%, and localization of computation related to the memories can increase the available bandwidth on internal buses by a factor of two. The main observation of our study is that there is a critical lack of tools and methods that support an integrated approach to partitioning, scheduling and arbitration, and data transfer management for such system implementations. Copyright 2003 ACM.
Today, the need for a high-speed bi-directional network in avionics is more evident than ever. The need for this proposal is driven by the shortcomings in current avionics architectures based on the MIL-STD-1553 data ...
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Today, the need for a high-speed bi-directional network in avionics is more evident than ever. The need for this proposal is driven by the shortcomings in current avionics architectures based on the MIL-STD-1553 data communication system and associated video distribution network, as well as the current ANSI standard Fiber Channel. Fiber Channel has the potential to be the only network protocol needed for avionics, but to maximize its life cycle cost benefits, a bi-directional implementation is required.
eLoom is an open source graph simulation software tool, developed at the University of New Mexico (UNM), that enables users to specify and simulate neural network models. Its specification language and libraries enabl...
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eLoom is an open source graph simulation software tool, developed at the University of New Mexico (UNM), that enables users to specify and simulate neural network models. Its specification language and libraries enables users to construct and simulate arbitrary, potentially hierarchical network structures on serial and parallel processing systems. In addition, eLoom is integrated with UNM's Flatland, an open source virtual environments development tool to provide real-time visualizations of the network structure and activity. Visualization is a useful method for understanding both learning and computation in artificial neural networks. Through 3D animated pictorially representations of the state and flow of information in the network, a better understanding of network functionality is achieved. ART-1, LAPART-II, MLP, and SOM neural networks are presented to illustrate eLoom and Mallard's capabilities. (C) 2003 Elsevier Science Ltd. All rights reserved.
By implementing an extensible network protocol stack in the AMP operating system, we have demonstrated support for fine-grained replacement of low-level network processing components. Furthermore, our compiler technol...
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By implementing an extensible network protocol stack in the AMP operating system, we have demonstrated support for fine-grained replacement of low-level network processing components. Furthermore, our compiler technology approach provides the means to support multiple hardware architectures and a framework for enforcing safety and security properties during runtime code generation. Our preliminary results indicate that our approach is feasible, has comparable per-packet processing costs to static code, and has acceptable per-module loading and code generation costs.
This paper provides the novel symbolically oriented approach to open access transmission network congestion management Transactions are the dc load flow symbolic simulator generated transfer functions. Congestion mana...
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Over the past several years, in response to demands from users that access to networked information and services be both customized and rapid, services and content have increasingly been 'pushed' towards netwo...
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Over the past several years, in response to demands from users that access to networked information and services be both customized and rapid, services and content have increasingly been 'pushed' towards network edges by providers. In turn, this has resulted in an increased need for a software framework that provides an open and systematic way for providers to deploy and manage such network edge services. We have implemented and tested such a framework in our Lab, called the Telcordia Edge Services Node. Inspired by the foundations built in the IETF OPES-related drafts, it is beneficial to Access, Service, and Content Providers alike. This paper reports on the design, implementation, and lessons-learned from our edge service node framework and management tools.
Visualization is a useful method for understanding both learning and computation in artificial neural networks. There are a large number of parameters in a neural network. By viewing these parameters pictorially, a be...
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Visualization is a useful method for understanding both learning and computation in artificial neural networks. There are a large number of parameters in a neural network. By viewing these parameters pictorially, a better understanding can be gained of how a network maps inputs to outputs. eLoom is an open source graph simulation tool, developed at the University of New Mexico, that enables users to specify and simulate various neural network models. Its specification language enables users to construct and simulate arbitrary, potentially hierarchical network structures on serial and parallel processing systems. In addition, eLoom is integrated with UNM's Flatland, an open source virtual environments development tool to provide real-time visualizations of the network structure and activity. ART-1 and LAPART-II neural networks are presented to illustrate eLoom and Flatland's capabilities.
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