Hanjun Kim  

Professor
School of Electrical and Electronic Engineering, Yonsei University

Ph.D. 2013, Department of Computer Science, Princeton University

Office: Engineering Hall #3-C415
Phone: +82-2-2123-2770
Email: first_name at yonsei.ac.kr
 
 
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Refereed International Conference Publications

Thread-Aware Area-Efficient High-Level Synthesis Compiler for Embedded Devices [abstract] (IEEE Xplore)
Changsu Kim, Shinnung Jeong, Sungjun Cho, Yongwoo Lee, William Song, Youngsok Kim, and Hanjun Kim
Proceedings of the 2021 International Symposium on Code Generation and Optimization (CGO), March 2021.

In the embedded device market, custom hardware platforms such as an application specific integrated circuit (ASIC) and a field programmable gate array (FPGA) are attractive thanks to their high performance and power efficiency. However, its huge design costs make it challenging for manufacturers to timely launch new devices. High-level synthesis (HLS) helps significantly reduce the design costs by automating the translation of service algorithms into hardware logics; however, current HLS compilers do not fit well to embedded devices as they fail to produce area-efficient solutions while supporting concurrent events from diverse peripherals such as sensors, actuators and network modules. This paper proposes a new thread-aware HLS compiler named DURO that produces area-efficient embedded devices. DURO shares commonly-invoked functions and operators across different callers and threads with a new thread-aware area cost model, and thus effectively reduces the logic size. Moreover, DURO supports a variety of device peripherals by automatically integrating peripheral controllers and interfaces as peripheral drivers. The experiment results of six embedded devices with ten peripherals demonstrate that DURO reduces the area and energy dissipation of embedded devices by 28.5% and 25.3% compared with the designs generated by the state-of-the-art HLS compiler. This work also implements FPGA prototypes of the six devices using DURO, and the measurement results show 65.3% energy saving over Raspberry Pi Zero with slightly better computation performance.