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標(biāo)題: Titlebook: VLSI Placement and Routing: The PI Project; Alan T. Sherman Book 1989 Springer-Verlag New York Inc. 1989 VLSI.circuit.computer.constraint. [打印本頁]

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VLSI Placement and Routing: The PI Project978-1-4613-9658-1Series ISSN 0172-603X Series E-ISSN 2512-5486
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Monographs in Computer Sciencehttp://image.papertrans.cn/v/image/980099.jpg
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https://doi.org/10.1007/978-1-4613-9658-1VLSI; circuit; computer; constraint; logic
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The Placement Framework,PI begins the layout process by placing the modules on the chip. This chapter describes the framework in which the placement heuristics operate. In particular, this chapter describes the broad structure of the placement heuristics, PI’s placement problem, and a data structure known as the placement tree which supports the placement heuristics.
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Conclusion,istics. In addition, in providing a contextfor studying how to layout VLSI chips, the PI Project inspired asignificant amount of research on placement and routing (. [179]–[195]). This chapter offers some concluding rematks about the PIProject, summarizing its major contributions and discussing howwell it met its original objectives.
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Alan T. Shermanincrease, making the optimization of routing problems an NP-hard problem. When using traditional routing algorithms, accuracy and efficiency cannot be balanced. Recently, Deep Q-Network (DQN) has shown great potential for solving dynamic network problems. However, existing DQN-based routing solution
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Alan T. Shermane impact of the epidemic has brought new challenges to online education, and the effect of online teaching has sparked extensive research. Why online learning has not received satisfying learning effect? And how to improve the quality of online education have become the focus of research. To address
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Alan T. Shermanlimited visibility of the equipment’s operational perimeter, difficulty obtaining datasets, and high accuracy requirement. This research shows how machine learning methods such as convolutional neural networks (CNN) and transfer learning can tackle these challenges. Transfer learning works well when
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Alan T. Shermantechniques to improve a system on noisy, encoded audio; with significant improvements observed if the training/testing mismatch is large. We evaluate our findings on a South African call centre dataset that contains noisy, WAV49-encoded audio: MTR provides consistent improvements on this set, but th
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Alan T. Shermanots. This third perspective is the focal point of the paper. The argument is that due to . robot consciousness, and the possibility that the immoral treatment of social robots may morally harm human interactants, there is a unique moral relation between humans and social robots wherein human interac
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Alan T. Shermantments to the material, further reading and exercises. .As a practical book it will be welcomed by a wide audience including,those already experienced in AI, students with some background inprogramming who are taking an introductory course in AI, and lecturerslooking for a precise, professional and
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Alan T. Shermanresult, we propose the . (Quantitative Annotation Function-based Operators) class of operators, a subclass of ., and study the complexity of several problems related to their specification and application in revising knowledge bases. Finally, we present an algorithm for computing the probability tha
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0172-603X an excellent job of collecting and clearly presenting material that was previously available only in various theses, confer- ence papers, and memoranda. He has provided here a balanced and comprehensive presentation of the key ideas and techniques used in PI, discussing part of his own Ph. D. work
作者: 眨眼    時(shí)間: 2025-3-28 04:48
Introduction,r newly discovered RSA cryptosystem on an .MOS LSI chip.. Their design called for 40,000 MOS transistors to be placed in a rectangular region measuring 31λ × 365λ, where λ = 2 microns [226, 227]. To assist them in their sizable task of placing and interconnecting the transistors, the three researche
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Power-Ground Routing,sizing how PI’s layout model strongly influenced this strategy. This chapter also explains two component algorithms of particular interest. The first is a novel heuristic for separating the power and ground wires in a single layer of metal; the second is a new technique for widening the power and gr
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Conclusion,istics. In addition, in providing a contextfor studying how to layout VLSI chips, the PI Project inspired asignificant amount of research on placement and routing (. [179]–[195]). This chapter offers some concluding rematks about the PIProject, summarizing its major contributions and discussing howw
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Related Layout Systems, algorithms. This chapter also describes selected other layout systems—including the Magic, Phoenix, and TimberWolf systems—and points out many of the significant layout systems that came before and after PI.
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Introduction,rchers to describe and modify components in high-level fashion. From this experience of laying out the first RSA chip grew Rivest’s inspiration for the .—an experimental project to explore automatic algorithmic approaches for laying out VLSI chips.




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