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Energy efficiency and robustness of advanced machine learning architectures :a cross-layer approach /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Energy efficiency and robustness of advanced machine learning architectures :Alberto Marchisio, Muhammad Shafique.
Reminder of title:	a cross-layer approach /
Author:	Marchisio, Alberto,
other author:	Shafique, Muhammad,
Description:	xiv, 346 pages :illustrations ;24 cm.
Subject:	Deep learning (Machine learning)
ISBN:	9781032855509

Energy efficiency and robustness of advanced machine learning architectures :a cross-layer approach /
Marchisio, Alberto,

Energy efficiency and robustness of advanced machine learning architectures :a cross-layer approach /Alberto Marchisio, Muhammad Shafique. - xiv, 346 pages :illustrations ;24 cm. - Chapman & Hall/CRC artificial intelligence and robotics series.

Includes bibliographical references and index.

Hardware and software optimizations for capsule networks -- Adversarial security threats for DNNs and CapsNets -- Inetration and of multiple and design objectives into NAS frameworks for CapsNets and DNNs -- Efficient optimizations for spiking neural networks on neuromorphic hardware -- Security threats for SNNs on discrete and event-based data.

"Machine Learning (ML) algorithms have shown a high level of accuracy, and applications are widely used in many systems and platforms. However, developing efficient ML-based systems requires addressing three problems: energy-efficiency, robustness, and techniques that typically focus on optimizing for a single objective/have a limited set of goals"--

ISBN: 9781032855509

LCCN: 2024023579Subjects--Topical Terms:

913129
Deep learning (Machine learning)

LC Class. No.: Q325.73 / .M35 2025

Dewey Class. No.: 006.3/1

Energy efficiency and robustness of advanced machine learning architectures :a cross-layer approach /
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245 1 0 $a Energy efficiency and robustness of advanced machine learning architectures : $b a cross-layer approach / $c Alberto Marchisio, Muhammad Shafique.
264 1 $a Boca Raton : $b CRC Press, Taylor & Francis Group, $c 2025.
300 $a xiv, 346 pages : $b illustrations ; $c 24 cm.
336 $a text $b txt $2 rdacontent
337 $a unmediated $b n $2 rdamedia
338 $a volume $b nc $2 rdacarrier
490 0 $a Chapman & Hall/CRC artificial intelligence and robotics series
504 $a Includes bibliographical references and index.
505 0 # $a Hardware and software optimizations for capsule networks -- Adversarial security threats for DNNs and CapsNets -- Inetration and of multiple and design objectives into NAS frameworks for CapsNets and DNNs -- Efficient optimizations for spiking neural networks on neuromorphic hardware -- Security threats for SNNs on discrete and event-based data.
520 # $a "Machine Learning (ML) algorithms have shown a high level of accuracy, and applications are widely used in many systems and platforms. However, developing efficient ML-based systems requires addressing three problems: energy-efficiency, robustness, and techniques that typically focus on optimizing for a single objective/have a limited set of goals"-- $c Provided by publisher.
650 # 0 $a Deep learning (Machine learning) $3 913129
650 # 0 $a Neural networks (Computer science) $3 181982
650 # 0 $a Robust optimization. $3 747261
650 # 0 $a Computer network architectures. $3 197806
650 # 0 $a Computer networks $x Energy consumption. $3 1009471
650 # 0 $a Computer networks $x Security measures. $3 185597
650 # 6 $a Apprentissage profond. $3 1009472
650 # 6 $a Réseaux neuronaux (Informatique) $3 1009520
650 # 6 $a Optimisation robuste. $3 1009473
650 # 6 $a Réseaux d'ordinateurs $x Architectures. $3 1009521
650 # 6 $a Réseaux d'ordinateurs $x Sécurité $x Mesures. $3 1009522
700 1 # $a Shafique, Muhammad, $e author. $3 1009470
776 0 8 $i Online version: $a Marchisio, Alberto. $t Energy efficiency and robustness of advanced machine learning architectures $d Boca Raton : CRC Press, 2025 $z 9781003530459 $w (DLC) 2024023580
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