普渡大学Anand教授学术讲座通知
时间:2016-1-27 19:29:20   阅读:   标签: 航天学院 讲座通知

 应航天学院微电子科学与技术系邀请,普渡大学电子与计算机工程系的教授Anand Raghunathan于1月29日来我校进行学术访问。

      访问期间,Anand Raghunathan教授将为本校师生做有关计算科学和电子技术在现代计算领域的前沿学术讲座,介绍面向近似计算的集成系统的电路、架构及软件设计技术,并与航天学院微电子科学与技术系的老师、博士及硕士研究生进行学术交流,探讨微电子的最新研究方向、拓展我校相关专业师生科研思路和国际视野,促进与国际知名高校和研究机构之间的交流与合作,欢迎广大师生踊跃参加。
时间:2016年1月29日10:00~11:00
地点:活动中心216
讲座者:Anand Raghunathan
讲座主题:《Approximate Computing and the Quest for Computing Efficiency》
讲座内容:
      The gap created by diminishing benefits from technology scaling on the one hand, and projected growth in computing demand on the other, has led to a quest for new sources of efficiency in computing. Fortunately, many of the workloads that are driving demand across the computing spectrum also present new opportunities. At the server end, computing demand is driven by the need to organize, analyze, interpret, and search through exploding amounts of data from the virtual and physical worlds. In mobile devices and deeply embedded systems, the creation and consumption of richer media and the need to interact more naturally and intelligently with users and the environment drive much of the computing demand. These applications are largely not about calculating a precise numerical end result; for them, ``correctness'' is defined as producing results that are good enough, or of sufficient quality. As a result, these workloads demonstrate a high degree of intrinsic resilience to their underlying computations being executed in an approximate or inexact manner. However, the design of computing platforms is still guided by the dogma that every computation must be executed with the same strict notion of correctness.
      Approximate computing broadly refers to exploiting the forgiving nature (or intrinsic resilience) of applications to design more efficient (faster, lower power) computing platforms. In this talk, we will describe how workload trends are driving interest in approximate computing, describe a vision for approximate computing at all layers of the computing stack, and outline a range of approximate computing techniques that we have developed spanning circuits, architecture, and software. We will conclude with a discussion of some of the challenges that need to be addressed in order to facilitate a broader adoption of approximate computing.
来访者简介:
      Anand Raghunathan教授,印度工业大学学士学位,普渡大学硕士和博士学位,现为普渡大学电子与计算工程系教授。主要研究领域包括SoC设计、专用体系结构、后CMOS器件计算和异构并行计算。Anand Raghunathan教授担任过多次专业领域的各大会议(CASES, ISLPED, VTS, and VLSI Design)的lead和chair,以及IEEE和ACM旗下的各大期刊的主编。目前是IEEE fellow和ACM Golden Core Member。
      Prof. Raghunathan received the B. Tech. degree from the Indian Institute of Technology, Madras, and the M.A. and Ph.D. degrees from Princeton University. He is a Professor of Electrical and Computer Engineering at Purdue University, where he directs research in the Integrated Systems Laboratory. His current areas of research include system-on-chip design, domain-specific architecture, computing with post-CMOS devices, and heterogeneous parallel computing. He is also co-founder and Director of Hardware at High Performance Imaging, Inc., a company formed to commercialize innovations in the area of computational imaging.
      Prof. Raghunathan has been a member of the technical program and organizing committees of several leading conferences and workshops, chaired premier IEEE/ACM conferences (CASES, ISLPED, VTS, and VLSI Design), and served on the editorial boards of various IEEE and ACM journals in his areas of interest. He received the IEEE Meritorious Service Award and Outstanding Service Award. He is a Fellow of the IEEE and Golden Core Member of the IEEE Computer Society. 
 
发布:王义 |  审核:曲法义 |  来源: 航天学院