I'm currently an ECE Ph.D candidate at the University of Michigan, Ann Arbor, planning to graduate in 2020. I received my M.S. degree in EE from the University of California, Los Angeles (UCLA) in 2015 and my B.S. degree in EE from National Tsing Hua University (NTHU) in 2012. My research focuses on domain-specific computing architectures and paradigms for accelerating machine learning, deep learning, computer vision and robotics.
In 2018, I took a gap year from my Ph.D. to serve as a Technical Founding Partner of Iluvatar CoreX, an AI chip and systems startup based in the Silicon Valley and Greater China. My past experiences encapsulate R&D, corporate strategy, product management and venture capital in the field of AI and the semiconductor industry.
I will be joining McKinsey & Company as an Associate upon graduation. Please hit me up on LinkedIn to connect!
EDUCATION
University of Michigan, Ann Arbor
Ph.D. in Electrical and Computer Engineering
May 2016 - Sep 2020
Ann Arbor, MI, USA
University of California, Los Angeles
M.S. in Electrical Engineering
Sep 2013 - May 2015
Los Angeles, CA, USA
National Tsing Hua University
B.S. in Electrical Engineering
Sep 2008 - Jun 2012
Hsinchu, TW
WORK EXPERIENCES
GigaDevice
Corporate Strategy Intern
Apr 2020 - Present
San Jose, CA, USA
COPi Group
Co-Founder, General Manager
Apr 2020 - Present
New York, NY, USA
Wolverine Venture Fund
Investment Associate
Oct 2019 - Jan 2020
Ann Arbor, MI, USA
Iluvatar CoreX (天数智芯)
Technical Founding Partner, Head of Advanced Technologies Group
Dec 2017 - Jan 2019
San Jose, CA, USA
Nvidia Research
Research Intern
Jun 2017 - Sep 2017
Santa Clara, CA, USA
Intel Labs
Senior Research Scientist
Graduate Technical Intern
May 2015 - May 2016
Santa Clara, CA, USA
Sep 2014 - Dec 2014
Silicon Image
ASIC Design Intern
Jun 2014 - Sep 2014
Sunnyvale, CA, USA
Skyviia
SoC Design Intern
Jun 2010 - Sep 2010
Hsinchu, TW
HONORS & AWARDS
Oct 2017
$50,000 Startup Award
TUS International Limited Gap Fund
$100,000 Startup Award
Michigan Translational Research and Commercialization (MTRAC)
Jun 2017
Sep 2016 - Sep 2020
Rackham Graduate Fellowship
University of Michigan, Ann Arbor
PUBLICATIONS
SNAP: A 1.67-21.55TOPS/W Sparse Neural Acceleration Processor for Unstructured Sparse Deep Neural Network
Inference in 16nm CMOS
J-F. Zhang, C-E Lee, C. Liu, S. Shao, S. Keckler, Z. Zhang
2019 Symposium on VLSI Circuits (VLSIC), Kyoto, Japan, 2019
A 135mW 1.70TOPS Sparse Video Sequence Inference SoC for Action Classification
T.Chen, C-E Lee, Z. Zhang
Journal of Solid-State Circuits (JSSC) , 2019
Towards Fast and Energy-Efficient Binarized Neural Network Inference on FPGA
C. Fu, S. Zhu, H. Su, C-E Lee, J. Zhao
International Symposium on Field-Programmable Gate Arrays (FPGA) 2019, Monterey Bay, US, 2019
Stitch-X: An Accelerator Architecture for Exploiting Unstructured Sparsity in Deep Neural Networks
C-E Lee, S. Shao, J-F Zhang, A. Parashar, J. Emer, S. Keckler, Z. Zhang
Conference on Systems and Machine Learning (SysML), Stanford, US, 2018
A 127mW 1.63TOPS Sparse Spatio-Temporal Cognitive SoC for Action Classification and Motion Tracking in Videos
C-E Lee, T.Chen, Z. Zhang
2017 Symposium on VLSI Circuits (VLSIC), Kyoto, Japan, 2017.
High-performance Spiking Neural Net Accelerators for Embedded Computer Vision Applications
JK. Kim, P, Knag, T. Chen, C. Liu, C-E Lee, Z. Zhang
IEEE SOI-3D-Subthreshold Microelectronics Technology Unified Conference (S3S), San Jose, US, 2017
An Error-Compensated Piecewise Linear Logarithmic Arithmetic Unit for Phong Lighting Acceleration
C-E Lee, M.D. Ercegovac
IEEE Proc. 49th Asilomar Conference on Signals, Systems & Computers (ACSSC), Asilomar, US, 2015
Reconfigurable and Selectively-Adaptive Signal Processing for Multi-Mode Wireless Communication
F. Sheikh, O. Andersson, C-E Lee, F. Xue, C-H Chen, A. Vaidya, A. Sharma, T. Tetzlaff
IEEE International Workshop on Signal Processing Systems (SiPs), Hangzhou, China, 2015
PATENTS
Methods and Apparatus for Similar Data Reuse in Dataflow Processing Systems (pending
Two-Level, Fine-Grained Programmable Architecture to Accelerate Deep Learning Workloads (pending)
Methods and Apparatus for Designing Flexible Dataflow Processor for Artificial Intelligent Devices
A Deep Neural Network Accelerator With Fine-Grained Parallelism Discovery
Sparse Video Inference Processor For Action Classification And Motion Tracking
Methods and Devices for Self-Interference Cancelation
RLS-DCD Adaptation Hardware Accelerator for Interference Cancellation in Full-Duplex Wireless Systems
Energy Efficient Polynomial Kernel Generation in Full-Duplex Radio Communication
