In the detailed routing for VLSI standard cell layout design, the over-the-cell channel routing, which utilizes the over-the-cell legion as the routing region, has been proposed. In this design method, after determini...
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In the detailed routing for VLSI standard cell layout design, the over-the-cell channel routing, which utilizes the over-the-cell legion as the routing region, has been proposed. In this design method, after determining the net assignment to each channel in the global routing step, the track assignment both in channel and on over-the-cell regions is performed by an over-the-cell channel router. However, conventional global routing algorithms do not assume over-the-cell channel routing but conventional channel routing, Therefore, the minimization of channel density does not always successfully lead to the minimization of the final channel height. This paper presents a new global Touting method for standard cell layouts to determine global routes for each net in both channel and over-the-cell regions simultaneously. The standard cell layout design system GLORIA based on the proposed algorithm is developed, and the experimental results compared with the conventional global routing method that performs the over-the-cell channel routing after global-routing with TimberWolf-SC4.2c are reported. Experimental results showed that the proposed routing algorithm can route about 18.4 percent fewer number of tracks than the conventional routing method, and the effectiveness of the proposed method is shown. The experimental results of applying the proposed method to the three-layer routing layout model also are presented.
This paper describes the implementation of transmission-line matrix (TLM) method algorithms on a massively parallel computer (DECmpp 12000), the technique of distributedcomputing in the UNIX environment, and the comb...
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This paper describes the implementation of transmission-line matrix (TLM) method algorithms on a massively parallel computer (DECmpp 12000), the technique of distributedcomputing in the UNIX environment, and the combination of TLM analysis with Prony's method as well as with autoregressive moving average (ARMA) digital signal processing for electromagnetic field modelling. By combining these advanced computation techniques, typical electromagnetic field modelling of microwave structures by TLM analysis can be accelerated by a few orders of magnitude.
Proposes a simple paradigm for constructing heuristics for the static assignment of parallel programs onto asynchronous, distributed memory, multiprocessor architectures. The proposed paradigm involves capturing the d...
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Proposes a simple paradigm for constructing heuristics for the static assignment of parallel programs onto asynchronous, distributed memory, multiprocessor architectures. The proposed paradigm involves capturing the dominant computation and communication components of an application and using this relatively simpler program representation to determine an assignment. Thus, the mapping problem is reduced from its most general form to a simpler form which often has optimal solutions.< >
Despite rapid growth in workstation and networking technologies, the workstation environment continues to pose challenging problems to shared processing. In this paper, we present a computational model and system for ...
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Despite rapid growth in workstation and networking technologies, the workstation environment continues to pose challenging problems to shared processing. In this paper, we present a computational model and system for the generation of distributed applications in such an environment. The well-known RPC model is modified by a novel concept known as template attachment. A computation consists of a network of sequential procedures which have been encapsulated in templates. A small selection of templates is available from which a distributed application with the desired communication behavior can be rapidly built. The system generates all the required low-level code for correct synchronization, communication, and scheduling. This results in a system that is easy to use and flexible, and can provide a programmer with the desired amount of control in using idle processing power over a network of workstations. The practical feasibility of the model has been demonstrated by implementing it for Unix1-based workstation environments.
Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are su...
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Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact their Quality of Service and violate their Service Level Agreements. Existing research on performance anomaly detection for edge computing environments focuses on model training approaches that either achieve high accuracy at the expense of a time-consuming and resource-intensive training process or prioritize training efficiency at the cost of lower accuracy. To address this gap, while considering the resource constraints and the large number of devices in modern edge platforms, we propose two clustering-based model training approaches: (1) intra-cluster parameter transfer learning-based model training (ICPTL) and (2) cluster-level model training (CM). These approaches aim to find a trade-off between the training efficiency of anomaly detection models and their accuracy. We compared the models trained under ICPTL and CM to models trained for specific devices (most accurate, least efficient) and a single general model trained for all devices (least accurate, most efficient). Our findings show that ICPTL’s model accuracy is comparable to that of the model per device approach while requiring only 40% of the training time. In addition, CM further improves training efficiency by requiring 23% less training time and reducing the number of trained models by approximately 66% compared to ICPTL, yet achieving a higher accuracy than a single general model.
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