September 21, 2018, 02:00 PM
Professor Xiaochen Guo: Toward "More Moore" and "More than Moore" in Memory Systems
[Friday, September 21, 2018 at 2:00 PM in Wegmans Hall 1400]
High density and low cost have been considered as the first-priority optimization goal for commodity DRAM. As memory technology continues to scale (More Moore), energy efficiency has become a pressing issue. DRAM energy consumption contributes a significant part of the system total energy. Row activation is one of the most energy inefficient components in DRAM. Prior work on reducing DRAM row activation energy relies on increasing the number of local wires to avoid performance degradation, which adds unaffordable area overheads. This talk will present our recent work on interleaved I/O, which allows data transferring from different partially activated banks to share the global I/O. The proposed DRAM architecture enables half-, quarter-, or one-eighth- page activations without adding wires. The second part of the talk will introduce our on-going work on supporting emerging memory features (More than Moore). The last decade has seen an explosion in new and innovative memory technologies. While certain technologies, like transactional memory, have seen adoption at the language level, others, such as sandboxed memory, scratchpad memory, and persistent memory, have not received systematic support. Our on-going work aims to provide unified system supports to enable easy adoption and fast prototype of emerging memory features.
Xiaochen Guo is an Assistant Professor in the Department of Electrical and Computer Engineering at Lehigh University. Dr. Guo received her Ph.D. degree in Electrical and Computer Engineering from the University of Rochester in 2015, and B.S. degree from Beihang University in 2009. Dr. Guo's research interests are in the broad area of computer architecture, with an emphasis on leveraging emerging technologies to build energy-efficient architectures. Dr. Guo is a recipient of IBM Ph.D. Fellowship, P. C. Rossin Assistant Professorship, the National Science Foundation CAREER Award, and the Lawrence Berkeley National Laboratory Computing Sciences Research Pathways Fellowship.
[Monday, October 22, 2018 at 8:30 AM in Wegmans Hall 2506]
Machine Learning, as a branch of Artificial Intelligence, is now starting to contribute towards every aspects of human life. It provides a powerful set of mathematical tools to facilitate the scientific explorations over a large volume of data. In combination with the recent development of powerful machine learning techniques and the abundance of audio-visual data of human life, it is now becoming possible to detect human behaviors using a complete data-centric approach. In this thesis we explore this possibility through a number of experiments in the context of public speaking.
This exploration has been conducted from several directions. First, we use the machine learning models to analyze the body language of the speakers. We capture the repetitive body movement patterns using an unsupervised technique. We design an interface namedAutoMannerthat uses these patterns to help the public speakers become aware of their idiosyncratic body movements. Second, we analyze the effects of narrative trajectories or the styles of story telling on the viewers of public speaking. Third, we predicted the TED talk ratings using a classical machine learning approach and a deep neural network based approach. We show that the neural network based approach can predict human behaviors with better accuracy than the classical machine learning approach. Additionally, the neural networks require comparatively lesser application-domain-specific knowledge. Consequently, the neural network approach could be utilized not only in the public speaking domain, but also in a wider range of other application areas. Finally, we design a real-time intervention technique to provide live information to the speakers while minimizing distraction.
Reception with light refreshments to follow at 10:30am in Wegmans Hall 2506
Advisor: Professor M. Ehsan Hoque (CS)
Committee: Professor Mark Bocko (ECE), Professor Gonzalo Mateos (ECE), Professor Daniel Gildea (CS)