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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Analog IC Placement Generation Via Neural Networks from Unlabeled Data

Analog IC Placement Generation Via Neural Networks from Unlabeled Data

Gusmão, António
Horta, Nuno
Lourenço, Nuno

Paperback

Series: Springerbriefs in Applied Sciences and Technology

General ComputersProbability & Statistics

ISBN10: 3030500608
ISBN13: 9783030500603
Publisher: Springer
Published: Jul 1 2020
Pages: 87
Weight: 0.35
Height: 0.22 Width: 6.14 Depth: 9.21
Language: English
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.

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