Machine Learning for Analytics Architecture: AI to Design AI, by Dr. Chris Lee

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Machine Learning for Analytics Architecture: AI to Design AI, by Dr. Chris Lee


Abstract

Niklaus Emil Wirth introduced the innovative idea that Programming = Algorithm + Data Structure. Inspired by this, we advance the concept to the next level by stating that Design = Algorithm + Architecture. With concurrent exploration of algorithm and architecture entitled Algorithm/Architecture Co-exploration (AAC), this methodology introduces a leading paradigm shift in advanced system design from System-on-a-Chip to Cloud and Edge. 

As algorithms with high accuracy become exceedingly more complex and Edge/IoT generated data becomes increasingly bigger, flexible parallel/reconfigurable processing are crucial in the design of efficient signal processing systems having low power. Hence the analysis of algorithms and data for potential computing in parallel, efficient data storage and data transfer is crucial. With extension of AAC for SoC system designs to even more versatile platforms based on analytics architecture, system scope is readily extensible to cognitive cloud and reconfigurable edge computing for multimedia and mobile health, a cross-level-of abstraction topic which will be introduced in this tutorial together with case studies.



  Date and Time

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  • Date: 26 May 2021
  • Time: 08:00 PM to 09:30 PM
  • All times are Mexico/General
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  • Guadalajara, Jalisco
  • Mexico

  • Starts 26 April 2021 11:45 AM
  • Ends 26 May 2021 11:45 PM
  • All times are Mexico/General
  • No Admission Charge


  Speakers

Dr. Chris Lee

Dr. Chris Lee of National Cheng Kung University (NCKU)

Topic:

Machine Learning for Analytics Architecture: AI to Design AI, by Chris Lee

Abstract:

Niklaus Emil Wirth introduced the innovative idea that Programming = Algorithm + Data Structure. Inspired by this, we advance the concept to the next level by stating that Design = Algorithm + Architecture. With concurrent exploration of algorithm and architecture entitled Algorithm/Architecture Co-exploration (AAC), this methodology introduces a leading paradigm shift in advanced system design from System-on-a-Chip to Cloud and Edge. 

As algorithms with high accuracy become exceedingly more complex and Edge/IoT generated data becomes increasingly bigger, flexible parallel/reconfigurable processing are crucial in the design of efficient signal processing systems having low power. Hence the analysis of algorithms and data for potential computing in parallel, efficient data storage and data transfer is crucial. With extension of AAC for SoC system designs to even more versatile platforms based on analytics architecture, system scope is readily extensible to cognitive cloud and reconfigurable edge computing for multimedia and mobile health, a cross-level-of abstraction topic which will be introduced in this tutorial together with case studies.

Biography:

Dr. Chris Gwo Giun Lee is an investigator in signal processing systems for multimedia and bioinformatics. His work on analytics of algorithm concurrently with architecture, Algorithm/Architecture Co-Design (AAC), has made possible accurate and efficient computations on SoC, cloud and edge including Digital Health. He is currently using AI to Design AI and is also enabling accessible health and wellness via AI Humanity.

His work have contributed to 130+ original research and technical publications with invention of 50+ patents worldwide.  His AAC work resulted in industry deployment of 60+ million LCD panels worldwide.  Two patents were licensed by US health industry for development of analytics platform based precision medicine products (Boston, MA, June 1, 2015, GLOBE NEWSWIRE).  This AAC work has been pivotal in delivering international standards, e.g. Reconfigurable Video Coding 3D extension of HEVC in MPEG.   

Dr. Chris was a system architect in Philips Semiconductor and also project leader in the Silicon Valley.  He was recruited to NCKU in 2003. He received his BSEE from National Taiwan University, MSEE and PH.DEE from the University of Massachusetts. Chris serves as the AE for and JSPS. He was formerly the AE for IEEE TSP IEEE and TCSVT for which he received the Best Associate Editor’s Award in 2011. He has served as the Distinguished Lecturer for IEEE CASS from 2019 ~ 2021. He was the Chair for IEEE Region 10, Industry Relations Committee, 2019 ~ 2020.

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