Energy Efficiency and Robustness of Advanced Machine Learning Architectures : A Cross-Layer Approach
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 fo...
| Autores principales: | , |
|---|---|
| Formato: | Libro |
| Publicado: |
Chapman and Hall/CRC
2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=3952443&site=ehost-live header: @attributes: shortDbName: nlebk uiTerm: 3952443 longDbName: eBook Collection (EBSCOhost) uiTag: AN controlInfo: bkinfo: btl: Energy Efficiency and Robustness of Advanced Machine Learning Architectures : A Cross-Layer Approach aug: au: Alberto Marchisio Muhammad Shafique sertl: Chapman and Hall/CRC Artificial Intelligence and Robotics Series isbn: 9781032855509 9781032870137 9781003530459 9781040165034 9781040165065 imageinfo: pubinfo: dt: @attributes: year: 2025 month: 01 day: 01 dtAvail: @attributes: year: 2024 month: 12 day: 04 pub: Chapman and Hall/CRC pubContract: CRC Press (Unlimited) place: [S.l.] price: 0.01 limitsGroup: maxCheckoutDays: 1500 copyPages: -1 pda: N printPagesOffline: 60 printPagesOnline: 60 previewPages: 10000 prePubGroup: dewey: @attributes: class: 006.31 item: 006 .31 lc: @attributes: class: Q325.5 item: Q 325 .5 artinfo: ui: 3952443 1456117011 formats: fmt: – @attributes: type: EB doid: NL$3952443$PDF caption: PDF download: Y – @attributes: type: EK doid: NL$3952443$EPUB caption: EPUB download: Y tig: atl: Energy Efficiency and Robustness of Advanced Machine Learning Architectures : A Cross-Layer Approach ptl: Energy Efficiency and Robustness of Advanced Machine Learning Architectures aug: au: Alberto Marchisio Muhammad Shafique su: Machine learning sug: subj: COMPUTERS / Artificial Intelligence / General COMPUTERS / Computer Engineering COMPUTERS / Data Science / Machine Learning Machine learning ab: 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.This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks. More specifically, this book improves the energy efficiency of complex models like CapsNets, through a specialized flow of hardware-level designs and software-level optimizations exploiting the application-driven knowledge of these systems and the error tolerance through approximations and quantization. This book also improves the robustness of ML models, in particular for SNNs executed on neuromorphic hardware, due to their inherent cost-effective features. This book integrates multiple optimization objectives into specialized frameworks for jointly optimizing the robustness and energy efficiency of these systems.This is an important resource for students and researchers of computer and electrical engineering who are interested in developing energy efficient and robust ML.The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license. pubtype: eBook doctype: Book ougenre: Book language: English copyright: @attributes: flag: N copyrightText: holdings: @attributes: islocal: N |
|---|