Po-Han Chen
Ph.D. Student in Electrical Engineering, admitted Winter 2021
Bio
Po-Han Chen is an EE Ph.D. student at Stanford University supervised by Prof. Priyanka Raina. He received his B.S. in Electrical Engineering and Computer Science (EECS) and M.S. in Electrical Engineering from National Tsing Hua University (Taiwan) in 2016 and 2018 respectively. Before joining Stanford, he was a digital circuit designer at MediaTek where he worked on developing hardware architectures of image processing pipeline. He is interested in designing hardware accelerators. Most of his previous works were related to computational photography algorithms such as digital refocusing. Currently, He is focusing on analyzing and designing architecture of CGRAs to create high-performance, energy-efficient, and reconfigurable computing platforms.
All Publications
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Amber: A 16-nm System-on-Chip With a Coarse-Grained Reconfigurable Array for Flexible Acceleration of Dense Linear Algebra
IEEE JOURNAL OF SOLID-STATE CIRCUITS
2023
View details for DOI 10.1109/JSSC.2023.3313116
View details for Web of Science ID 001078350700001
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AHA: An Agile Approach to the Design of Coarse-Grained Reconfigurable Accelerators and Compilers
ACM Transactions on Embedded Computing Systems
2023; 22 (2)
View details for DOI 10.1145/3534933
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A 250-mW 5.4G-Rendered-Pixel/s Realistic Refocusing Processor for High-Performance Five-Camera Mobile Devices
IEEE OPEN JOURNAL OF THE SOLID-STATE CIRCUITS SOCIETY
2023; 3: 52-62
View details for DOI 10.1109/OJSSCS.2023.3244759
View details for Web of Science ID 001356016800001
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An Open-Source 4x8 Coarse-Grained Reconfigurable Array Using SkyWater 130 nm Technology and Agile Hardware Design Flow
IEEE. 2023
View details for DOI 10.1109/ISCAS46773.2023.10182052
View details for Web of Science ID 001038214602125
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mflowgen: a modular flow generator and ecosystem for community-driven physical design
DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference
2022: 1339–1342
View details for DOI 10.1145/3489517.3530633
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SAPIENS: A 64-kb RRAM-Based Non-Volatile Associative Memory for One-Shot Learning and Inference at the Edge
IEEE TRANSACTIONS ON ELECTRON DEVICES
2021; 68 (12): 6637-6643
View details for DOI 10.1109/TED.2021.3110464
View details for Web of Science ID 000724501000107