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Date: October 1st, 2020
“Validation of Power Amplifiers & Transmitters in the 5G Era Using Modulated Signals,” CASS-SCV Online Lecture Event

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Events on January, 2020

“Visual Data Analytics for Dietary Assessment and Video Compression” by Dr. Fengqing Maggie Zhu

Date: January 29th, 2020

Visual Data Analytics for Dietary Assessment and Video Compression

Fengqing Maggie Zhu

Assistant Professor, School of Electrical and Computer Engineering, Purdue University

January 29 @ 6:30 pm – 8:30 pm PST

Event Sponsored and Organized By:

IEEE Signal Processing Society, Chapter of Santa Clara Valley

Co-Sponsors:

IEEE Information Theory Society, SCV Chapter

IEEE Circuit and Systems Society, SCV Chapter

Registration: Here.

Agenda:

6:30pm-7:00pm: Registration, Food, Networking

7:00pm-8:00pm: Talk

8:00pm-8:30pm: Q&A and Networking

Location:

Santa Clara University, California Misson Room in Benson Memorial Center820 Alviso Street, Santa Clara, CA, 95050

Parking information will be posted shortly.

Abstract

One of the most important behavior both socially and culturally that impacts one’s health is diet. Assessing dietary intake of children and adults with high accuracy remains a major challenge in

the field of nutrition and health. In this talk, I will present the design and development of a novel food record using a mobile device to provide an accurate measure of daily food and nutrient intake. Different computer vision and machine learning methods have been developed to automatically identify foods and estimate energy from a meal image. These methods have been validated on both public datasets and our own datasets collected from dietary studies. Significant advances in video coding has been developed in the last two decades to satisfy the growing requirements of video applications. In this talk, we introduce the design and development of a switchable region-based coding scheme that leverages semantic segmentation to achieve a superior coding efficiency. We also present a novel perceptual quality assessment measure for the region-based coding scheme since point-by-point metrics are inadequate. The proposed method is evaluated on both standard test sets and the YouTube UGC datasets, which shows significant data rate reduction with satisfying visual quality verified by a subjective study.

Biography

Fengqing Maggie Zhu is an Assistant Professor of Electrical and Computer Engineering at Purdue University, West Lafayette, Indiana. Dr. Zhu received the B.S.E.E. (with highest distinction), M.S. and Ph.D. degrees in Electrical and Computer Engineering from Purdue University in 2004, 2006 and 2011, respectively. Her research interests include image processing and analysis, video compression and computer vision. Prior to joining Purdue in 2015, she was a Staff Researcher at Futurewei Technologies (USA), where she received a Certification of Recognition for Core Technology Contribution in 2012. She is the recipient of an NSF CISE Research Initiation Initiative (CRII) award in 2017, and a Google Faculty Research Award in 2019.


“Scalable Deep Neural Network Accelerator Design and Methodology” by Dr. Prof. Y. Sophia Shao

Date: January 16th, 2020

Description

Sponsored by:

IEEE Silicon Valley Solid-State Circuits Society (SSCS)

IEEE Silicon Valley Circuits and Systems Society (CASS)

“Scalable Deep Neural Network Accelerator Design and Methodology”

Prof. Y. Sophia Shao

University of California, Berkeley

Registration Link: Here.

Abstract

Machine learning systems are being widely deployed across billions of edge devices and datacenter across the world. At the same time, in the absence of Moore’s Law and Dennard scaling, we rely on building vertically integrated systems with domain-specific accelerators to improve the system performance and efficiency. In this talk, I will describe our recent work on building scalable and efficient hardware that delivers real-time and robust performance across diverse deployment scenarios through joint hardware-software optimizations. I will conclude my talk by describing ongoing efforts toward building next-generation computing platforms for real-time machine learning.

Bio

Prof. Y. Sophia Shao is an Assistant Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley. Previously, she was a Senior Research Scientist at NVIDIA. She received her Ph.D. degree in 2016 and S.M. degree in 2014 from Harvard University and a B.S. degree in Electrical Engineering from Zhejiang University, China. Her research interests are in the area of computer architecture, with a special focus on domain-specific architecture, deep-learning accelerators, and high-productivity hardware design methodology. Her work has been selected as one of the TopPicks in Computer Architecture, a MICRO Best Paper award, and her Ph.D. dissertation was nominated by Harvard for ACM Doctoral Dissertation Award. She is a Siebel Scholar, an invited participant at the Rising Stars in EECS Workshop, and a recipient of the IBM Ph.D. Fellowship.

The seminar is FREE and donation is accepted for refreshments (FREE SSCS/CAS members/$2 IEEE members/$5 non-members)
Eventbrite registration is required for everyone to attend the talk.

Venue:

Texas Instruments Silicon Valley Auditorium 2900 Semiconductor Dr., Building E, Santa Clara, CA 95051 Directions and Map (to locate Building E).

Time: January 16 (Thursday) evening 6:00PM-8:00PM
Networking and Refreshments: 6:00 PM – 6:30 PM
Technical Talk: 6:30 PM – 8:00 PM


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