Analog IC Placement Generation via Neural Networks from Unlabeled Data

António Gusmão, Nuno Horta, Nuno Lourenço, Ricardo Martins (Autoren)

Buch | Softcover

XIII, 87 Seiten

2020 | 1st ed. 2020
Springer International Publishing (Verlag)
978-3-030-50060-3 (ISBN)

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In this book, innovative research using artificial neural networks (ANNs) is conducted to automate the placement task in analog integrated circuit layout design, by creating a generalized model that can generate valid layouts at push-button speed. Further, it exploits ANNs' generalization and push-button speed prediction (once fully trained) capabilities, and details the optimal description of the input/output data relation. The description developed here is chiefly reflected in two of the system's characteristics: the shape of the input data and the minimized loss function. In order to address the latter, abstract and segmented descriptions of both the input data and the objective behavior are developed, which allow the model to identify, in newer scenarios, sub-blocks which can be found in the input data. This approach yields device-level descriptions of the input topology that, for each device, focus on describing its relation to every other device in the topology. By means of thesedescriptions, an unfamiliar overall topology can be broken down into devices that are subject to the same constraints as a device in one of the training topologies.

In the experimental results chapter, the trained ANNs are used to produce a variety of valid placement solutions even beyond the scope of the training/validation sets, demonstrating the model's effectiveness in terms of identifying common components between newer topologies and reutilizing the acquired knowledge. Lastly, the methodology used can readily adapt to the given problem's context (high label production cost), resulting in an efficient, inexpensive and fast model.

Introduction.- Related Work: Machine Learning and Electronic Design Automation.- Unlabeled Data and Artificial Neural Networks.- Placement Loss: Placement Constraints Description and Satisfiability Evaluation.- Experimental Results in Industrial Case Studies.- Conclusions.

Erscheinungsdatum	04.07.2020
Reihe/Serie	SpringerBriefs in Applied Sciences and Technology
Zusatzinfo	XIII, 87 p. 68 illus., 39 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	174 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	Analog IC Design Automation • Analog IC Placement • ANNS • Artificial Neural Networks • computer-aided-design tools • Electronic Design Automation • machine learning
ISBN-10	3-030-50060-8 / 3030500608
ISBN-13	978-3-030-50060-3 / 9783030500603
Zustand	Neuware