各位老師及同學您好：

(本場次對象為大三、大四跟研究所學生)

系上將於10/8邀請紐約雪城大學之系主任 Alex K. Jones 教授蒞臨演講，誠摯歡迎踴躍參與。

----------------------------------------------------
演講日期：10/8 (三) 10:30-11:30

演講地點：資訊工程系館 4263 4264教室

演講者： Alex K. Jones  https://ecs.syracuse.edu/faculty-staff/alex-k-jones

演講題目： Moore $caling, von Neumann Problems

演講人簡歷： 

Alex K. Jones is the Klaus Schroder Endowed Professor and Chair of the Department of Electrical Engineering and Computer Science (EECS) at Syracuse University, Syracuse, New York, USA. He received his B.S. degree in Physics in 1998 from the College of William and Mary in Williamsburg, VA, and his M.S. (2000) and Ph.D. (2002) degrees in Electrical and Computer Engineering from Northwestern University in Evanston, IL. Previously, he was a Full Professor (with tenure) of Electrical and Computer Engineering at the University of Pittsburgh, where he also held courtesy appointments as Professor of Computer Science and Professor of Physics & Astronomy. He recently served at the National Science Foundation (NSF), most recently as Deputy Division Director of the Electrical, Communications, and Cyber Systems (ECCS) Division, and earlier as Program Director and Cluster Lead for the Computer Systems Research (CSR) Cluster in the Computer and Network Systems (CNS) Division of the CISE Directorate. His research interests include compilation for configurable systems and architectures, scaled and emerging memory, reliability, fault tolerance, quantum computing, and sustainable computing. He is the author of more than 200 publications in these areas, with research supported by the NSF, DARPA, NSA, ARO, LPS, private foundations, and industry. He is active in program committees in computer architecture, design automation, and sustainable computing, serves as Steering Committee Chair for the IEEE International Green and Sustainable Computing Conference, is a Topical Editor for the IEEE Transactions on Computers and Associate Editor for the IEEE Transactions on Sustainable Computing, and is a Fellow of the IEEE, a Senior Member of the ACM, and a Member of the AAAS.

Talk abstract:
The slowing of Moore’s Law, the persistence of von Neumann bottlenecks, and the massive data demands of emerging applications such as AI call for new architectures that address both performance and sustainability. My research explores processing-in-memory (PIM) as a solution space to reduce data movement costs and improve efficiency, either replacing or supplementing accelerators. Using commodity DRAM as an exemplar, we developed a technology-agnostic PIM approach for multiplication and addition that achieves up to 10× speedup and 8× higher energy efficiency over prior in-DRAM proposals. With more advanced memories such as spintronic racetrack memory, we leverage transverse access to construct polymorphic multi-input gates for multi-operand logic, delivering up to 6.9× performance and 5.5× energy gains. Extending this to floating-point operations enables deep-learning acceleration at the edge, supporting both inference and training with ≥2× efficiency compared to FPGA accelerators. We further show that PIM-based solutions can outperform GPUs in reducing greenhouse gas emissions, underscoring new tradeoffs in sustainable edge AI system design. This work also reflects the broader opportunities at Syracuse University, where the EECS department is building on strengths in AI, wireless communications, and quantum systems to tackle critical technological challenges.