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题目：Transformer Acceleration with Full Stack Optimization

摘要

Recent years have witnessed the fast evolution of Transformer models in the field of computer vision, natural language processing, etc. Though promising, Transformer models usually require higher computation and memory as well as more diversed kernel supports. In this talk, I will discuss some of our recent works that leverage network/hardware co-design and co-optimization to improve the efficiency of Transformer models across different platforms. The talk will also discuss interesting future directions for Transformer and LLM acceleration.

嘉宾：李萌于2022年加入北京大学集成电路学院和人工智能研究院，任助理教授，博士生导师，获国家青年高层次人才计划（海外）。加入北京大学前，他曾任职于全球最大社交媒体公司Facebook的虚拟现实增强现实实验室，作为技术主管从事高效人工智能加速算法和系统的研究。他于2018年和2013年分别在美国德州大学奥斯汀分校和北京大学获得博士和学士学位。他的研究兴趣集中于高效、安全的多模态人工智能加速算法和系统。他在国际顶级会议、期刊发表文章70余篇，并于2017年和2018年获得IEEE HOST和ACM GLSVLSI的会议最佳论文。此外，他还获得了欧洲设计自动化协会最佳博士论文、ACM学生科研竞赛总决赛第一名等奖项。