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2019/9/11 2019 Emerging Technologies, and the Technology Hype Cycle

Date And Time

Wed, September 11, 2019

7:00 PM – 9:00 PM PDT (Registration starts at 6:30pm)

Organizer: CIE Emerging Technologies Group

Speaker: Dr. William Kao

Dr. William Kao received his BSEE, MSEE and PhD from the University of Illinois Urbana-Champaign. He worked in the Semiconductor and Electronic Design Automation industries for more than 30 years holding several senior and executive engineering management positions at Texas Instruments, Xerox Corporation, and Cadence Design Systems.

Dr. Kao has authored more than 40 technical papers at IEEE Journals and Conferences, and holds eight software and IC design patents. He was an Adjunct Professor at UCLA Electrical Engineering Department where he taught courses in computer aided IC design.

Dr. Kao is a Senior Member of IEEE, and was one of the founding members of IEEE-Circuits and Systems – Silicon Valley Chapter.

Dr. Kao teaches Clean Technology, Emerging Technologies and Business Sustainability courses at the University of California Santa Cruz, Silicon Valley Extension.

Dr. Kao current interests are the latest Emerging Technologies including Clean Technology, Renewable Energy, Big Data, IoT, Smart Cities, Sensor Networks, Innovation, Augmented Reality, Robotics, Artificial Intelligence, Machine Learning and 3D Printing. He frequently presents and discusses the above topics in a monthly seminar series.


The 2019 Hype Cycle highlights the emerging technologies with significant impact on business, society and people over the next five to 10 years. This year includes technologies that promise to deliver a global low-latency internet, create a virtual map of the real world and mimic human creativity.

Some of the technologies on the rise include artificial tissue, immersive workplaces, AR cloud, adaptive machine learning, nanoscale 3D printing, augmented intelligence, flying autonomous vehicles, emotion and explainable AI, synthetic data. Some of the technologies at the peak include edge AI, low earth orbit satellite systems, edge analytics, 5G, biochips and graph analytics. Finally technologies sliding into the trough include next generation memory, 3D sensing cameras, and autonomous driving.

This two hour seminar will present and explain the newest set of emerging technologies with an overview of each technology.

2019/9/4 CASPA Special Interest Group Seminar II: From 5,000X Model Compression to 50X Acceleration – Achieving Real-Time Execution of ALL DNNs on Mo

  • Date And Time: Wed, September 4, 2019  6:30 PM – 9:00 PM PDT
  • Speaker: Dr. Yanzhi Wang, Assistant Professor, Northeastern University

Dr. Yanzhi Wang is currently an assistant professor in the Department of Electrical and Computer Engineering at Northeastern University. He has received his Ph.D. Degree in Computer Engineering from University of Southern California (USC) in 2014, and his B.S. Degree with Distinction in Electronic Engineering from Tsinghua University in 2009.

Dr. Wang’s current research interests mainly focus on DNN model compression and energy-efficient implementation (on various platforms). His research maintains the highest model compression rates on representative DNNs since 09/2018. His work on AQFP superconducting based DNN acceleration is by far the highest energy efficiency among all hardware devices. His work has been published broadly in top conference and journal venues (e.g., ASPLOS, ISCA, MICRO, HPCA, ISSCC, AAAI, ICML, CVPR, ICLR, IJCAI, ECCV, ACM MM, DAC, ICCAD, FPGA, LCTES, CCS, VLDB, ICDCS, TComputer, TCAD, JSAC, Nature SP, etc.), and has been cited above 4,400 times. He has received four Best Paper Awards, has another seven Best Paper Nominations and three Popular Paper Awards.

  • Abstract:

This presentation focuses on two recent contributions on model compression and acceleration of deep neural networks (DNNs). The first is a systematic, unified DNN model compression framework based on the powerful optimization tool ADMM (Alternating Direction Methods of Multipliers), which applies to non-structured and various types of structured weight pruning as well as weight quantization technique of DNNs. It achieves unprecedented model compression rates on representative DNNs, consistently outperforming competing methods. When weight pruning and quantization are combined, we achieve up to 4,438X weight storage reduction without accuracy loss, which is two orders of magnitude higher than prior methods. Our most recent results conducted a comprehensive comparison between non-structured and structured weight pruning with quantization in place, and suggest that non-structured weight pruning is not desirable at any hardware platform.

However, using mobile devices as an example, we show that existing model compression techniques, even assisted by ADMM, are still difficult to translate into notable acceleration or real-time execution of DNNs. Therefore, we need to go beyond the existing model compression schemes, and develop novel schemes that are desirable for both algorithm and hardware. Compilers will act as the bridge between algorithm and hardware, maximizing parallelism and hardware performance. We develop a combination of pattern pruning and connectivity pruning, which is desirable at all of theory, algorithm, compiler, and hardware levels. We achieve 20ms inference time of large-scale DNN VGG-16 on smartphone without accuracy loss, which is 50X faster than TensorFlow-Lite. We can potentially enable real-time execution of all DNNs using the proposed framework.