IoT Sensing Device and Machine Learning

The interest in the Internet of Things (IoT) and the cognitive computing via machine learning is rapidly rising, and it is very natural for many to envision to combine those two. In several applications, such combination promises to enable new features, to improve accuracy in digital processing and autonomous decision, and to reduce wireless communication bandwidth and thus system power dissipation. Among several candidates, neural network (NN) based systems gain significant attention for several desirable characteristics including high accuracy, regularity, parallelism, and programmability. However, it indeed poses several challenges to include/implement such cognitive functions in/for IoT sensing devices due to the limited resources (hardware and energy) available in those devices. In this seminar, we will discuss those challenges, namely implementing machine-learning in resource-constrained IoT devices, and present our recent efforts across algorithm, hardware architecture, and circuits, and some combinations of those, to address the challenge.
Mingoo Seok is an assistant professor in the Department of Electrical Engineering, School of Engineering and Applied Science at Columbia University since 2012. He received the BS (with summa cum laude) in electrical engineering from Seoul National University, South Korea, in 2005, and the MS and PhD degree from University of Michigan in 2007 and 2011, respectively, all in electrical engineering. He has spent about a year as a member of technical staff in the Systems and Applications R&D Center of Texas Instruments, Dallas, Texas.

He has a research interest in energy-efficient and high performance VLSI circuit and system design, cyber physical systems, ubiquitous sensing and computing, and system on chip design. He received 1999 Distinguished Undergraduate Scholarship from the Korea Foundation for Advanced Studies, 2005 Doctoral Fellowship from the same organization, and 2008 Rackham Pre-Doctoral Fellowship from University of Michigan, Ann Arbor. He also won 2009 AMD/CICC Scholarship Award for picowatt voltage reference work and 2009 DAC/ISSCC Design Contest for the 35pW sensor platform design (a.k.a. Phoenix Processor). He holds one pending international patent.