Linghao Song

Linghao Song is an Assistant Professor in the Department of Electrical & Computer Engineering at Yale University. His research focuses on accelerator architecture design, FPGA-based acceleration systems, and ReRAM-based computing. He is affiliated with the School of Engineering & Applied Science and holds expertise in sparse matrix processing, high-level synthesis, and task-parallel programming frameworks.

Education: Ph.D. and M.S. from Duke University and University of Pittsburgh, respectively, with a B.S.E. from Shanghai Jiao Tong University.

Research Interests: Song's work spans FPGA-accelerated computing, resistive memory (ReRAM) architectures for deep learning and graph processing, and high-performance sparse matrix operations. His recent projects include the TAPA framework for FPGA programming, ReFloat for iterative linear solvers, and the Sextans/Serpens accelerators for sparse matrix computations.

Key Contributions: Over 20 peer-reviewed publications on topics like ReRAM-based accelerators (e.g., GraphR, PipeLayer), FPGA optimization frameworks (TAPA, RapidStream), and novel hardware designs for neural networks and graph analytics. His work emphasizes energy efficiency, scalability, and cross-domain applicability of hardware accelerators.

Awards: Recipient of the Duke ECE Outstanding Dissertation Award (2021), EDAA Outstanding Dissertation Award (2020), and National Scholarship of China (2012).

Labs/Teams: Leads research in FPGA acceleration and memory-based computing, collaborating on projects involving HBM integration, resistive memory architectures, and task-parallel dataflow frameworks.