Energy Efficiency and Robustness of Advanced Machine Learning Architectures: A Cross-Layer Approach
暫譯: 先進機器學習架構的能源效率與穩健性：跨層方法

本書透過利用先進機器學習（ML）模型的獨特特性來應對這些挑戰，並探討跨層概念和技術，以結合硬體和軟體層級的方法，構建穩健且節能的架構，適用於這些先進的機器學習網絡。

Alberto Marchisio received his B.Sc. and M.Sc. degrees in Electronic Engineering from Politecnico di Torino, Turin, Italy, in October 2015 and April 2018, respectively. He received his Ph.D. degree in Computer Science from the Technische Universität Wien (TU Wien) Informatics Doctoral College Resilient Embedded Systems, Vienna, Austria, in September 2023. Currently, he is a Research Group Leader with the eBrain Lab, Division of Engineering, New York University Abu Dhabi (NYUAD), United Arab Emirates. His main research interests include hardware and software optimizations for machine learning, brain-inspired computing, VLSI architecture design, emerging computing technologies, robust design, and approximate computing for energy efficiency. He (co-)authored 30+ papers in prestigious international conferences and journals. He received the honorable mention at the Italian National Finals of Maths Olympic Games in 2012, and the Richard Newton Young Fellow Award in 2019.

Muhammad Shafique (M'11 - SM'16) 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 collaborative R&D activities across the globe. Besides co-founding a technology startup in Pakistan, he was also an initiator and team lead of an ICT R&D project. He has also established strong research ties with multiple universities in worldwide, where he has been actively co-supervising various R&D activities and student/research Theses since 2011, resulting in top-quality research outcome and scientific publications. Before KIT, he was with Streaming Networks Pvt. Ltd. where he was involved in research and development of video coding systems several years. In October 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, Dr. Shafique is with the New York University (NYU), where he is currently a Full Professor and the director of eBrain Lab at the NYU-Abu Dhabi in UAE, and a Global Network Professor at the Tandon School of Engineering, NYU-New York City in USA. He is also a Co-PI/Investigator in multiple NYUAD Centers, including Center of Artificial Intelligence and Robotics (CAIR), Center of Cyber Security (CCS), Center for InTeractIng urban nEtworkS (CITIES), and Center for Quantum and Topological Systems (CQTS).

Dr. Shafique has demonstrated success in obtaining prestigious grants, 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 and machine learning algorithms. His research interests are in AI & machine learning hardware and system-level design, brain-inspired computing, neuromorphic computing, approximate computing, quantum machine learning, cognitive autonomous systems, robotics, wearable healthcare, AI for healthcare, energy-efficient systems, robust computing, machine learning secrity and privacy, hardware security, emerging technologies, electronic design automation, FPGAs, MPSoCs, embedded systems, and quantum computing. 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). He has served as the Associate Editor and Guest Editor of prestigious journals like IEEE Transactions on Computer Aided Design (TCAD), IEEE Design and Test Magazine (D&T), ACM Transactions on Embedded Computing (TECS), IEEE Transactions on Sustainable Computing (T-SUSC), and Elsevier MICPRO. He has served as the TPC Chair of several conferences like CODES+ISSS, IGSC, ISVLSI, PARMA-DITAM, RTML, ESTIMedia and LPDC; General Chair of ISVLSI, IGSC, 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, MICRO, ISCA, DATE, CASES, ASPDAC, and FPL. He has been recognized as a member of the ACM TODAES Distinguished Review Board in 2022. He 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. He holds one US patent and has (co-)authored 7 Books, 20+ Book Chapters, 350+ papers in premier journals and conferences, and over 100 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, 2022 and 2023, the ATRC's ASPIRE Award for Research Excellence in 2021, 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. His research work on aging optimization for GPUs featured as a Research Highlight in the Nature Electronics, Feb.2018 issue. Dr. Shafique was named in the NYU's 2021 Faculty Honors List. His students have also secured many prestigious student and research awards in the research community

**阿爾貝托·馬爾基西奧**於2015年10月和2018年4月分別在意大利都靈理工大學獲得電子工程學士和碩士學位。他於2023年9月在奧地利維也納的維也納科技大學（Technische Universität Wien, TU Wien）獲得計算機科學博士學位。目前，他是阿布達比紐約大學（New York University Abu Dhabi, NYUAD）工程系eBrain Lab的研究小組負責人。他的主要研究興趣包括機器學習的硬體和軟體優化、腦啟發計算、VLSI架構設計、新興計算技術、穩健設計以及為能源效率而進行的近似計算。他（共同）撰寫了30多篇在國際知名會議和期刊上發表的論文。他在2012年獲得意大利數學奧林匹克全國決賽的榮譽提名，並於2019年獲得理查德·牛頓青年研究員獎。

**穆罕默德·沙菲克**（M'11 - SM'16）於2011年在德國卡爾斯魯厄理工學院（Karlsruhe Institute of Technology, KIT）獲得計算機科學博士學位。此後，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合作研發活動。除了在巴基斯坦共同創辦一家科技初創公司外，他還是ICT研發項目的發起人和團隊負責人。他與多所全球大學建立了強有力的研究聯繫，自2011年以來，他一直積極共同指導各種研發活動和學生/研究論文，取得了高品質的研究成果和科學出版物。在加入KIT之前，他曾在Streaming Networks Pvt. Ltd.工作，參與了視頻編碼系統的研究和開發多年。2016年10月，他加入奧地利維也納的維也納科技大學計算機工程學院，擔任計算機架構和穩健、能源高效技術的全職教授。自2020年9月以來，沙菲克博士在紐約大學（NYU）任教，目前是阿布達比NYU的eBrain Lab主任及全職教授，並在美國紐約市的Tandon工程學院擔任全球網絡教授。他還是多個NYUAD中心的共同主要研究者/調查員，包括人工智慧與機器人中心（Center of Artificial Intelligence and Robotics, CAIR）、網絡安全中心（Center of Cyber Security, CCS）、城市互動網絡中心（Center for InTeractIng urban nEtworkS, CITIES）和量子與拓撲系統中心（Center for Quantum and Topological Systems, CQTS）。

沙菲克博士在獲得知名獎助金、領導團隊項目、按時完成演示、激勵團隊成員達到最佳表現以及完成獨立挑戰性任務方面表現出色。他的經驗得到了強大的技術知識和教育背景的支持（始終是金牌得主）。他對各種視頻編碼標準和機器學習算法有深入的理解。他的研究興趣包括AI與機器學習硬體和系統級設計、腦啟發計算、神經形態計算、近似計算、量子機器學習、認知自主系統、機器人技術、可穿戴健康護理、醫療保健的AI、能源高效系統、穩健計算、機器學習安全與隱私、硬體安全、新興技術、電子設計自動化、FPGA、MPSoC、嵌入式系統和量子計算。他的研究特別關注計算和記憶系統的跨層分析、建模、設計和優化。所研究的技術和工具被應用於物聯網（IoT）、智能網絡物理系統（CPS）和ICT發展（ICT4D）領域的應用案例中。

沙菲克博士在多個重要場合發表了幾次主題演講、受邀演講和教程。他還在多個旗艦會議（如DAC、ICCAD、DATE、IOLTS和ESWeek）組織了許多特別會議。他曾擔任多個知名期刊的副編輯和客座編輯，如IEEE計算機輔助設計學報（IEEE Transactions on Computer Aided Design, TCAD）、IEEE設計與測試雜誌（IEEE Design and Test Magazine, D&T）、ACM嵌入式計算學報（ACM Transactions on Embedded Computing, TECS）、IEEE可持續計算學報（IEEE Transactions on Sustainable Computing, T-SUSC）和Elsevier MICPRO。他曾擔任多個會議的TPC主席，如CODES+ISSS、IGSC、ISVLSI、PARMA-DITAM、RTML、ESTIMedia和LPDC；ISVLSI、IGSC、DDECS和ESTIMedia的總主席；DAC、ICCAD、DATE、IOLTS、DSD和FDL的分會主席；以及ISVLSI的博士論壇主席。他還在多個知名的IEEE/ACM會議的程序委員會中任職，包括ICCAD、DAC、MICRO、ISCA、DATE、CASES、ASPDAC和FPL。他在2022年被認可為ACM TODAES傑出評審委員會成員。他是IEEE和IEEE信號處理學會（SPS）的資深會員，也是ACM、SIGARCH、SIGDA、SIGBED和HIPEAC的專業會員。他擁有一項美國專利，並（共同）撰寫了7本書、20多個書章、350多篇在主要期刊和會議上發表的論文，以及超過100篇存檔文章。

沙菲克博士獲得了2015年ACM/SIGDA傑出新教員獎、2020年AI-2000芯片技術最具影響力學者獎（2022年和2023年）、2021年ATRC的ASPIRE研究卓越獎、六枚金牌以及在CODES+ISSS、DATE、DAC和ICCAD等知名會議上獲得的多個最佳論文獎和提名、最佳碩士論文獎、DAC'14設計師賽道最佳海報獎、IEEE計算機學報「本月特色論文」獎和最佳講師獎。他在2018年2月的《自然電子學》（Nature Electronics）期刊上發表的有關GPU老化優化的研究工作被列為研究亮點。沙菲克博士被列入紐約大學2021年教職員榮譽名單。他的學生在研究界也獲得了許多知名的學生和研究獎項。