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001 978-3-030-50061-0
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005 20240423125237.0
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008 200630s2020 sz | s |||| 0|eng d
020 _a9783030500610
_9978-3-030-50061-0
024 7 _a10.1007/978-3-030-50061-0
_2doi
050 4 _aQ325.5-.7
072 7 _aUYQM
_2bicssc
072 7 _aMAT029000
_2bisacsh
072 7 _aUYQM
_2thema
082 0 4 _a006.31
_223
100 1 _aGusmão, António.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aAnalog IC Placement Generation via Neural Networks from Unlabeled Data
_h[electronic resource] /
_cby António Gusmão, Nuno Horta, Nuno Lourenço, Ricardo Martins.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXIII, 87 p. 68 illus., 39 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Applied Sciences and Technology,
_x2191-5318
505 0 _aIntroduction -- 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. .
520 _aIn 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. .
650 0 _aMachine learning.
650 1 4 _aMachine Learning.
700 1 _aHorta, Nuno.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aLourenço, Nuno.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aMartins, Ricardo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030500603
776 0 8 _iPrinted edition:
_z9783030500627
830 0 _aSpringerBriefs in Applied Sciences and Technology,
_x2191-5318
856 4 0 _uhttps://doi.org/10.1007/978-3-030-50061-0
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c175818
_d175818