專用集成電路與系統國家重點實驗室
講座信息：Computer Architecture and System Innovations for Enabling AI-based IoT Applications
報告人：Tao Li, Professor, University of Florida, IEEE Fellow
時間🏄🏻♂️： 2018年8月14日下午2:00-4:00
地點：張江校區微電子樓389室
Abstract:
In recent years, the artificial intelligence (AI) techniques, represented by deep neural networks (DNN), have demonstrated transformative impacts to modern Internet-of-Things (IoT) applications such as smart cities and smart transportation. With the increasing computing power and energy efficiency of mobile devices, there is a growing interest in performing AI-based IoT applications on mobile platforms. As a result, we believe the next-generation AI-based applications are pervasive across all platforms, ranging from central cloud data center to edge-side wearable and mobile devices.
However, we observe several architectural gaps that challenge the pervasive AI. First, the diversity of computing hardware resources and different end-user requirements present challenges to AI-based applications deployment on various IoT platforms, which results in inferior user satisfaction. Second, the traditional statically trained DNN model could not efficiently handle the dynamic data in the real IoT environments, which leads to low inference accuracy. Lastly, the training of DNN models still involves extensive human efforts to collect and label the large-scale dataset, which becomes impractical in IoT big data era where raw IoT data is largely un-labeled and un-categorized.
In this talk, I will introduce our recent research which enables pervasive AI-based IoT applications to become high-efficient, user-satisfactory, and intelligent. I will first introduce Pervasive AI, a user satisfaction-aware deep learning inference framework, to provide the best user satisfaction when migrating AI-based applications from Cloud to all kinds of platforms. Next, I will describe In-situ AI, a novel computing paradigm tailored to AI-based IoT applications. Finally, to achieve real intelligent (support autonomous learning) in IoT nodes, I will introduce an unsupervised GAN-based deep learning accelerator.
報告人簡歷📔：
李濤博士是美國佛羅裏達大學(University of Florida)工程沐鸣電子與計算機工程系正教授(終身教職)，首批傑出教授獲得者，智能計算機體系結構設計實驗室主任，IEEE Fellow。李濤博士2004年於美國德克薩斯大學奧斯汀分校獲得計算機工程博士學位。2015年8月-2017年12月但任美國國家自然基金委員會(NSF)計算機信息科學與工程(CISE)學部項目主任。主要研究方向為計算機系統架構(含數據中心🧲、雲計算、大數據👩🏻🦽、物聯網👨🏿🦲🐹、及機器學習軟硬件系統)和前瞻計算技術(類腦計算🤴、內存計算、量子計算)，高性能計算機體系結構(多核、GPU 、加速器)👨🏻🔬、 高效／可靠／低功耗微處理器及存儲系統🧁、面向雲計算和大數據數據中心、虛擬化🧑🏻🦱🍻、並行與分布式計算🧑🏻🦳、新型及可重構計算架構、面向特定應用(如🕸：AI、大數據圖計算、SDN/NFV)計算架構與芯片設計、多核容錯處理器、片上互連網絡、面向多眾核的可擴展服務器體系架構⭐️、新型前瞻技術及應用對硬件，操作系統及系統軟件的影響🐻、嵌入式與片上系統🌥、以及計算機系統性能評估等🕋。
李濤博士在著名的國際期刊和計算機體系結構類一級國際會議 ISCA👡、MICRO🔲、HPCA🥿🙅🏿♀️、ALPLOS、SIGMETRICS❓、PACT、DSN發表論文120余篇👨🏻🦲，獲ICCD’17🎣、Computer Architecture Letters’ 15💁🏿💮、HPCA’11最佳論文獎。另外8篇論文被HPCA’18、HPCA’17🧚🏽、ICPP’37230359🤾、CGO’14、DSN’11🦸🏿♂️、MICRO’08、IISWC’07 和 MASCOTS’06會議程序委員會推薦參選“最佳論文獎”。李濤教授是HPCA名人堂(HPCA Hall of Fame)排名第一的全球華人科學家。 2013年獲Yahoo！重大研究計劃挑戰獎🔞。2009年獲美國國家科學基金會傑出青年教授獎(NSF CAREER Award)🙍🏼。2008年⚾️，2007年，2006年均獲 IBM 沐鸣獎（IBM Faculty Award）。2008年獲得美國微軟研究院安全及可擴展多核計算機獎 (Safe and Scalable Multi-core Computing Award)🙍🏻。2006年獲得微軟研究院可信計算課程研究獎(Trustworthy Computing Curriculum Award)📅🙂↕️。 2012, 2014兩度獲佛羅裏達大學工程沐鸣年度最佳博士生論文導師獎。
聯系人：韓軍
