当前位置:首页 > 报告详情

Session 1Plenary.pdf

上传人: 张** 编号:620784 2025-03-31 34页 14.67MB

1、Session 1 Overview:Plenary I NVI TED PAPERSChair:Edith Beign Meta,Menlo Park,CA ISSCC Conference ChairAssociate Chair:Thomas Burd Advanced Micro Devices,Santa Clara,CA ISSCC International Technical-Program Chair8 2025 IEEE International Solid-State Circuits ConferenceI SSCC 2025/SESSI ON 1/PLENARY/O

2、VERVI EW979-8-3315-4101-9/25/$31.00 2025 IEEEThe Plenary Session starts with welcoming remarks and introduction from the Conference Chair,Edith Beign,followed by the International Technical Program Chair,Thomas Burd,providing an overview of ISSCC 2025.The Plenary Session will feature four distinguis

3、hed keynote speakers,who are leaders and pioneers in their domain,covering together a broad spectrum of our industry.An Awards Ceremony will take place after the first two Plenary talks to recognize major technical and professional accomplishments presented by the IEEE,Solid-State Circuits Society(S

4、SCS),and ISSCC.The first plenary talk“AI Era Innovation Matrix”is by Navid Shahriari,Senior Vice President of Foundry Technology Development at Intel.This presentation describes the significant innovation needed from transistors to software to enable AI systems to continue their rapid rate of perfor

5、mance scaling.This innovation encompasses a matrix of technologies,including process technology,packaging with advanced 3D integration,interconnect at the board and system levels,power delivery across the system and to high-wattage SoCs,system hardware architecture,and a co-designed software stack.T

6、he second plenary talk“From Chips to Thoughts:Building Physical Intelligence into Robotic Systems”is by Daniela Rus,Director of the Computer Science and Artificial Intelligence Laboratory(CSAIL)and the Andrew(1956)and Erna Viterbi Professor in EECS at the Massachusetts Institute of Technology.This p

word格式文档无特别注明外均可编辑修改,预览文件经过压缩,下载原文更清晰!
三个皮匠报告文库所有资源均是客户上传分享,仅供网友学习交流,未经上传用户书面授权,请勿作商用。
本文主要讨论了人工智能(AI)技术在快速发展中面临的能源挑战,以及如何通过创新来提高AI系统的能效。主要观点包括: 1. AI系统,尤其是大型语言模型和多模态系统,在训练和部署过程中需要巨大的计算资源,导致高能耗和碳排放。例如,训练一个218亿参数的语言模型产生的碳排放相当于几辆汽车整个生命周期的排放。 2. 为了解决AI的能源挑战,正在开发几种方法,包括专门为AI工作负载设计的硬件(如TPU和边缘AI芯片),这些硬件比通用处理器(如CPU或GPU)能效更高;以及模仿人脑低功耗并行处理能力的神经形态计算。 3. 优化服务器硬件,包括先进的冷却和电源管理系统,可以进一步减少AI数据中心操作的能源足迹。此外,将计算从大型能源密集型数据中心转移到本地设备(“边缘”设备)可以减少与传输大量数据到中央云服务器相关的能源成本。 4. 为了实现AI的可持续发展,需要建立明确的能效指标,如每瓦浮点运算次数和每推理能耗,并设定AI能效的雄心勃勃的目标。
如何在AI时代实现技术创新? 如何构建具有物理智能的机器人系统? 内存技术创新如何推动AI革命?
客服
商务合作
小程序
服务号
折叠