题目：A Cross-Layer Perspective for Energy Efficient Processing - From beyond-CMOS devices to deep learning（高效能处理的跨层设计视角—从超越CMOS器件到深度学习）

主讲人：X. Sharon Hu 教授 ，美国圣母大学， IEEE Fellow

时间：2019年6月24日（星期一），上午9：30-11：30

地点：信息科学与工程学院114教室

Abstract：As Moore’s Law based device scaling and accompanying performance scaling trends are slowing down, there is increasing interest in new technologies and computational models for fast and more energy-efficient information processing. Meanwhile, there is growing evidence that, with respect to traditional Boolean circuits and von Neumann processors, it will be challenging for beyond-CMOS devices to compete with the CMOS technology. Exploiting unique characteristics of emerging devices, especially in the context of alternative circuit and architectural paradigms, has the potential to offer orders of magnitude improvement in terms of power, performance and capability. To take full advantage of beyond-CMOS devices, cross-layer efforts spanning from devices to circuits to architectures to algorithms are indispensable.

This talk will examine energy-efficient neural network accelerators for embedded applications in this context. Several deep neural network accelerator designs based on cross-layer efforts spanning from alternative device technologies, circuit styles and architectures will be highlighted. Application-level benchmarking studies will be presented. The discussions will demonstrate that cross-layer efforts indeed can lead to orders of magnitude gain towards achieving extreme scale energy-efficient processing.

报告摘要：

随着晶体管器件的特征尺寸接近物理极限使得摩尔定律放缓，越来越多高速率、高能效的新计算模型和信息处理技术引起业界的广泛关注。然而，基于传统的Boolean 逻辑电路和Von Neumann架构，超越 CMOS器件的性能和可靠性很难CMOS技术相媲美。因此，在新的电路设计和架构范式的背景下，充分利用新兴器件的本征特性，很有可能使得集成电路的能效比有数量级的提升。为了达到这一目标，需要从底层的材料及器件一直到顶层的算法及应用进行整体考虑，开展跨层设计研究与优化。

本次报告将以嵌入式应用中的高能效处理器为例，介绍几种深度神经网络加速器的器件、电路、系统结构联合设计的思路和方法；同时，报告将给出应用层面的benchmarking研究成果。通过案例可以进一步说明，跨层设计将是提升未来集成电路能效比的有效途径。

Bio

X. Sharon Hu is a professor in the department of Computer Science and Engineering at the University of Notre Dame, USA. Her research interests include low-power system design, circuit and architecture design with emerging technologies, hardware/software co-design and real-time embedded systems. She has published more than 300 papers in these areas. Some of her recognitions include the Best Paper Award from the Design Automation Conference and from the International Symposium on Low Power Electronics and Design, and the NSF CAREER award. She has participated in several large industry and government sponsored center-level projects and is a theme leader in an NSF/SRC E2CDA project. She is the General Chair of Design Automation Conference in 2018 and was the TPC chair of DAC in 2015. She also served as Associate Editor for IEEE Transactions on VLSI, ACM Transactions on Design Automation of Electronic Systems, etc. and is an Associate Editor of ACM Transactions on Cyber-Physical Systems. X. Sharon Hu is a Fellow of the IEEE.

报告人简介：

X. Sharon Hu 胡晓波

美国圣母大学计算机科学与工程系教授，IEEE Fellow，ACM SIGDA 主席。主要学术方向为低功耗系统设计、基于新兴技术的电路和架构设计、软硬件协同设计及嵌入式系统等，已发表论文300余篇，并获得Design Automation Conference， ACM/IEEE International Symposium on Low Power Electronics and Design 最佳论文奖，NSF 杰出成就奖（NSF CAREER Award）。她参与多项政府-企业联合资助的研究中心级别项目，包括担任NSF/SRC E2CDA项目的负责人。她还担任2018年度 ACM/IEEE Design Automation Conference大会主席，2015年度DAC TPC主席；IEEE Transactions on VLSI、ACM Transaction on Design Automation of Electronic Systems、 ACM Transactions on Embedded Computing Systems、ACM Transactions on Cyber-Physical Systems 等学术期刊的副主编。