Titlebook: Analog IC Placement Generation via Neural Networks from Unlabeled Data; António Gusm?o,Nuno Horta,Ricardo Martins Book 2020 The Author(s),

口語

書目名稱Analog IC Placement Generation via Neural Networks from Unlabeled Data影響因子(影響力)




書目名稱Analog IC Placement Generation via Neural Networks from Unlabeled Data影響因子(影響力)學科排名




書目名稱Analog IC Placement Generation via Neural Networks from Unlabeled Data網絡公開度




書目名稱Analog IC Placement Generation via Neural Networks from Unlabeled Data網絡公開度學科排名




書目名稱Analog IC Placement Generation via Neural Networks from Unlabeled Data被引頻次




書目名稱Analog IC Placement Generation via Neural Networks from Unlabeled Data被引頻次學科排名




書目名稱Analog IC Placement Generation via Neural Networks from Unlabeled Data年度引用




書目名稱Analog IC Placement Generation via Neural Networks from Unlabeled Data年度引用學科排名




書目名稱Analog IC Placement Generation via Neural Networks from Unlabeled Data讀者反饋




書目名稱Analog IC Placement Generation via Neural Networks from Unlabeled Data讀者反饋學科排名

Arable

Artificial Neural Network Overview,arison of the different used methods for each of these components is made, such as the functioning of each neuron, the learning process that makes use of optimization tools, the hyperparameters that define the model’s architecture, and the influence of the selected features.

Calculus

GRE

Analog IC Placement Generation via Neural Networks from Unlabeled Data978-3-030-50061-0Series ISSN 2191-530X Series E-ISSN 2191-5318

intention

https://doi.org/10.1007/978-3-322-99411-0ration and the limitations that the current design flow faces. The standard procedures are presented. Furthermore, the concept of machine learning and the use of this branch of artificial intelligence as a step towards the production of electronic design automation tools for analog and mixed signal integrated circuits is introduced.

FOLD

Hans-Jürgen Dotzler,Siegfried Schickarison of the different used methods for each of these components is made, such as the functioning of each neuron, the learning process that makes use of optimization tools, the hyperparameters that define the model’s architecture, and the influence of the selected features.

Isometric

Hans-Jürgen Dotzler,Siegfried Schickictions. Attention is placed into the development of the input features in order to expand the solution’s scope and increase generalization, and, the introduction of a new loss function that evaluates the prediction made through the fulfillment of the circuit’s topological constraints.

偽書

Heribert Meffert,Ralf BirkelbachIn this chapter, the constraints which influence the process of layout generation are explained, along with the description of four main approaches of existing EDA tools for analog placement/for analog IC layout.

BILK

https://doi.org/10.1007/978-3-322-83625-0This chapter details the tests and analysis performed on the different ANN models. These models differ by the format of their input vector or by the loss function used during training, while the network’s architecture is kept the same. The objective is to compare the impact of these key parts of the model.

甜得發(fā)膩

https://doi.org/10.1007/978-3-322-83625-0This chapter wraps up this book and present some future directions for further applications of ANNs towards the automation of the placement process of analog integrated circuit layout design.