Analog IC placement generation via neural networks from unlabeled data / António Gusmão, Nuno Horta, Nuno Lourenço, Ricardo Martins.
2020
TK7874
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Title
Analog IC placement generation via neural networks from unlabeled data / António Gusmão, Nuno Horta, Nuno Lourenço, Ricardo Martins.
Author
Gusmão, António.
ISBN
9783030500610 (electronic book)
3030500616 (electronic book)
9783030500603
3030500616 (electronic book)
9783030500603
Publication Details
Cham : Springer, 2020.
Language
English
Description
1 online resource (96 pages).
Item Number
10.1007/978-3-030-50
Call Number
TK7874
Dewey Decimal Classification
621.3815
Summary
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 systems 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 these descriptions, 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 models 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 problems context (high label production cost), resulting in an efficient, inexpensive and fast model.
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Source of Description
Description based on print version record.
Added Author
Horta, Nuno C. G.
Lourenço, Nuno.
Martins, Ricardo.
Lourenço, Nuno.
Martins, Ricardo.
Series
SpringerBriefs in applied sciences and technology.
Available in Other Form
Analog IC Placement Generation Via Neural Networks from Unlabeled Data
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