Embedded Machine Learning for Cyber-Physical, Iot, and Edge Computing: Hardware Architectures
Pasricha, Sudeep, Shafique, Muhammad
- 出版商: Springer
- 出版日期: 2024-10-03
- 售價: $3,360
- 貴賓價: 9.5 折 $3,192
- 語言: 英文
- 頁數: 412
- 裝訂: Quality Paper - also called trade paper
- ISBN: 3031195701
- ISBN-13: 9783031195709
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相關分類:
嵌入式系統、Machine Learning、物聯網 IoT
海外代購書籍(需單獨結帳)
相關主題
商品描述
This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits.
商品描述(中文翻譯)
本書介紹了在資源受限系統上有效實現機器學習模型的最新進展,涵蓋不同的應用領域。重點在於展示將機器學習應用於創新應用領域的有趣且新穎的案例,探索高效機器學習加速器的高效硬體設計、記憶體優化技術,說明模型壓縮和神經架構搜尋技術,以實現資源受限硬體平台上的能源效率和快速執行,並理解硬體-軟體共同設計技術,以獲得更大的能源、可靠性和性能效益。
作者簡介
Muhammad Shafique received his Ph.D. degree in computer science from the Karlsruhe Institute of Technology (KIT), Germany, in 2011. Afterwards, he established and led a highly recognized research group at KIT for several years as well as conducted impactful R&D activities across the globe. Before KIT, he was with Streaming Networks Pvt. Ltd. where he was involved in research and development of video coding systems several years. In Oct.2016, he joined the Institute of Computer Engineering at the Faculty of Informatics, Technische Universität Wien (TU Wien), Vienna, Austria as a Full Professor of Computer Architecture and Robust, Energy-Efficient Technologies. Since Sep.2020, he is with the Division of Engineering at New York University Abu Dhabi (NYU-AD) in UAE, and is a Global Network faculty at the NYU's Tandon School of Engineering (NYU-NY) in USA. He is the director of the eBrain research lab, and is also a Co-PI/Investigator in multiple large-scale research centers at NYUAD, including the Center of Artificial Intelligence and Robotics (CAIR), Center for Quantum and Topological Systems, Center of Cyber Security (CCS), and Center for InTeractIng urban nEtworkS (CITIES). Dr. Shafique has demonstrated success in leading team-projects, meeting deadlines for demonstrations, motivating team members to peak performance levels, and completion of independent challenging tasks. His experience is corroborated by strong technical knowledge and an educational record (throughout Gold Medalist). He also possesses an in-depth understanding of various video coding standards. His research interests are in brain-inspired computing, AI & machine learning hardware and system-level design, autonomous systems, wearable healthcare, energy-efficient systems, robust computing, hardware security, emerging technologies, FPGAs, MPSoCs, and embedded systems. His research has a special focus on cross-layer analysis, modeling, design, and optimization of computing and memory systems. The researched technologies and tools are deployed in application use cases from Internet-of-Things (IoT), smart Cyber-Physical Systems (CPS), and ICT for Development (ICT4D) domains. Dr. Shafique has given several Keynotes, Invited Talks, and Tutorials at premier venues. He has also organized many special sessions at flagship conferences (like DAC, ICCAD, DATE, IOLTS, and ESWeek), and has served as the Guest Editor for IEEE Design and Test Magazine (D&T), IEEE Transactions on Sustainable Computing (T-SUSC), IEEE Transactions on Embedded Computing (TECS), and Elsevier MICPRO. He has served as the TPC Chair of several conferences like IGSC, ISVLSI, PARMA-DITAM, RTML, ESTIMedia and LPDC; General Chair of ISVLSI, DDECS and ESTIMedia; Track Chair at DAC, ICCAD, DATE, IOLTS, DSD and FDL; and PhD Forum Chair of ISVLSI. He has also served on the program committees of numerous prestigious IEEE/ACM conferences including ICCAD, DAC, ISCA, DATE, CASES, ASPDAC, and FPL. He holds one US patent and has (co-)authored 6 Books, 15+ Book Chapters, 300+ papers in premier journals and conferences, and over 50 archive articles. Dr. Shafique received the prestigious 2015 ACM/SIGDA Outstanding New Faculty Award, the AI-2000 Chip Technology Most Influential Scholar Award in 2020, six gold medals in his educational career, and several best paper awards and nominations at prestigious conferences like CODES+ISSS, DATE, DAC and ICCAD, Best Master Thesis Award, DAC'14 Designer Track Best Poster Award, IEEE Transactions of Computer "Feature Paper of the Month" Awards, and Best Lecturer Award. Dr. Shafique is a senior member of the IEEE and IEEE Signal Processing Society (SPS), and a professional member of the ACM, SIGARCH, SIGDA, SIGBED, and HIPEAC.
作者簡介(中文翻譯)
Sudeep Pasricha 是科羅拉多州立大學電子與計算機工程系、計算機科學系及系統工程系的 Walter Scott Jr. 工程學院教授。他是嵌入式、高效能及智能計算(EPIC)實驗室的主任及計算機工程系的系主任。Pasricha 教授在印度德里技術學院獲得電子與通信工程的學士學位,並在加州大學爾灣分校獲得計算機科學的博士學位。他在業界擁有數年的工作經驗,專注於電子晶片設計自動化、基於模型的設計及嵌入式系統共同設計。Pasricha 教授的研究獲得超過 700 萬美元的資金支持,來自多個贊助機構,包括 NSF、SRC、AFOSR、ORNL、國防部、Fiat-Chrysler 和 NASA。他共同撰寫了多本書籍,貢獻了幾個書章,並在同行評審的會議、期刊及書籍上發表了超過 250 篇研究文章。他參與了多個專題小組、主題演講,並在頂尖會議上組織了與其研究領域相關的特別會議和教程。他是 IEEE(計算機學會)的資深會員、ACM 的傑出會員及 ACM 傑出演講者。
Pasricha 教授的研究廣泛集中於軟體演算法、硬體架構及硬體-軟體共同設計,旨在實現能源高效、容錯、即時及安全的計算。這些努力針對多尺度計算平台,包括嵌入式系統和物聯網(IoT)系統、網路物理系統、行動裝置及數據中心。Pasricha 教授在多個 IEEE 和 ACM 會議上獲得 16 項最佳論文獎及提名,包括 DAC、ASPDAC、NOCS、GLSVLSI、SLIP、AICCSA 和 ISQED。其他顯著獎項包括:2019 年喬治·T·阿貝爾傑出研究教師獎、2016-2018 年大學傑出蒙福特教授獎、2016-2019 年 Walter Scott Jr. 工程學院 Rockwell-Anderson 教授獎、2018 年 IEEE-CS/TCVLSI 中期研究成就獎、2015 年 IEEE/TCSC 中期研究者卓越獎、2014 年喬治·T·阿貝爾傑出中期教師獎及 2013 年 AFOSR 年輕研究者獎。
目前,Pasricha 教授是 ACM SIGDA 的副主席及 ACM 新興計算技術期刊(JETC)的資深副編輯。他也是 ACM 嵌入式計算系統期刊(TECS)、IEEE 集成電路與系統計算機輔助設計期刊(TCAD)、IEEE 消費電子期刊(CM)及 IEEE 計算機設計與測試期刊(D&T)的副編輯。他還擔任 IEEE 可持續計算期刊(TSUSC)指導委員會的主席。他目前或曾擔任多個 IEEE/ACM 會議的組織委員會成員,如 DAC、ESWEEK、ICCAD、GLSVLSI、NOCS、RTCSA 等。他曾擔任多個 IEEE/ACM 會議的總主席,如 NOCS、HCW、IGSC、iSES、ICESS 等;以及 CODES+ISSS、NOCS、IGSC、iNIS、VLSID、HCW、DAC 博士論壇、ICCAD Cadathlon 等的程序主席。他也在多個 IEEE/ACM 會議的技術程序委員會中任職,如 DAC、DATE、ICCAD、ICCD、NOCS 等。他在加州大學爾灣分校的嵌入式與網路物理系統中心擔任附屬教職。他還因專業服務獲得多項獎項,包括 2019 年 ACM SIGDA 傑出服務獎、2015 年 ACM SIGDA 服務獎及 2012 年 ACM SIGDA 技術領導獎。
穆罕默德·沙菲克於 2011 年在德國卡爾斯魯厄理工學院(KIT)獲得計算機科學博士學位。之後,他在 KIT 建立並領導了一個備受認可的研究小組,並在全球範圍內進行了有影響力的研發活動。在加入 KIT 之前,他曾在 Streaming Networks 工作。