• Ph.D., Electrical and Computer Engineering
Georgia Institute of Technology, Atlanta, Georgia, USA
• B.S., Electrical and Electronic Engineering
Yonsei University, Seoul, South Korea
• Associate Professor, School of Electrical and Electronic Engineering
Yonsei University, Seoul, South Korea
• Senior Engineer, Pre‑Silicon Quality & Design Reliability Team
Intel, Santa Clara, California, USA
• Graduate Intern, Design for Power Team
Qualcomm, San Diego, California, USA
• Graduate Co‑op, Power and Reliability‑Aware Microarchitecture Team
IBM T.J. Watson Research Center, Yorktown Heights, New York, USA
• Graduate Co‑op, Research and Advanced Development Lab
AMD Research, Bellevue, Washington, USA
• Graduate Intern, Computer Science Department
Sandia National Laboratories, Albuquerque, New Mexico, USA
• Computer architecture and systems
• Heterogeneous computing
• Neural network accelerators
• Power and energy efficiency
• Thermal and lifetime reliability
• Hardware management and scheduling
• Memory architecture
• GPU microarchitecture
• Modeling and simulation techniques
• Teaching Excellence Award
College of Engineering, Yonsei University (2018, 2020, 2022)
• Distinguished Faculty Award for Teaching Excellence
Yonsei University (2018)
• Best Student Paper Award
IEEE International Reliability Physics Symposium (2016)
• IBM/SRC Graduate Fellowship (2012‑2015)
• H. Kim and W. Song, "LAS: Locality‑Aware Scheduling for GEMM‑Accelerated Convolutions in GPUs," IEEE Transactions on Parallel and Distributed Systems (TPDS), vol. 34, no. 5, May 2023, pp. 1479-1494.
• Y. Kim, H. Kim, and W. Song, "NOMAD: Enabling Non‑blocking OS‑Managed DRAM Cache via Tag‑Data Decoupling," IEEE International Symposium on High‑Performance Computer Architecture (HPCA), Feb. 2023, pp. 193-205.
• J. Lee, J. Lee, Y. Oh, W. Song, and W. Ro, "SnakeByte: A TLB Design with Adaptive and Recursive Page Merging in GPUs," IEEE International Symposium on High‑Performance Computer Architecture (HPCA), Feb. 2023, pp. 1195-1207.
• B. Kim, S. Lee, C. Park, H. Kim, and W. Song, "The Nebula Benchmark Suite: Implications of Lightweight Neural Networks," IEEE Transactions on Computers (TC), vol. 70, no. 11, Nov. 2021, pp. 1887‑1900.
• H. Kim, S. Ahn, B. Kim, Y. Oh, W. Ro, and W. Song, "Duplo: Lifting Redundant Memory Accesses of Deep Neural Networks for GPU Tensor Cores," IEEE/ACM International Symposium on Microarchitecture (MICRO), Oct. 2020, pp. 725‑737.
• Y. Oh, M. Yoon, W. Song, and W. Ro, "FineReg: Fine‑Grained Register File Management for Augmenting GPU Throughput," IEEE/ACM International Symposium on Microarchitecture (MICRO), Oct. 2018, pp. 364‑379.
• W. Song, S. Mukhopadhyay, and S. Yalamanchili, "Amdahl’s Law for Lifetime Reliability Scaling in Heterogeneous Multicore Processors," IEEE International Symposium on High‑Performance Computer Architecture (HPCA), Mar. 2016, pp. 594‑605.
• W. Song, C. Park, B. Kim, and S. Ryu, “Neural Network Computing Device and Control Method,” US17/883,010, Aug. 2022.
• W. Song, W. Ro, H. Kim, S. Ahn, Y. Oh, and B. Kim, “Operation Device of Convolutional Neural Network, Operation Method of Convolutional Neural Network and Computer Program Stored in A Recording Medium to Execute The Method,” US17/752,235, May 2022.
• S. O, W. Ro, W. Song, and J. Lee, "Control Unit, Computing System, and Method of Creating and Searching for Page Table Entry," US17/526391, Nov. 2021.
• R. Monfort, P. Bose, A. Buyuktosunoglu, C. Y. Cher, H. Jacobson, W. Song, K. Swaminathan, A. Vega, and L. Wang, "Optimization of Application Workflow in Mobile Embedded Devices," US9690555, June 2017.
• B. Kim, C. Park, T. Lim, and W. Song, "NPUsim: Full‑System, Cycle‑Accurate, Functional Simulations of Deep Neural Network Accelerators," US DOE Workshop on Modeling and Simulation of Systems and Applications, Oct. 2021.
• B. Kim, S. Lee, and W. Song, "Nebula: Lightweight Neural Network Benchmarks," US DOE Workshop on Modeling and Simulation of Systems and Applications, Aug. 2020.
• Chanho Park, Bogil Kim, Sungmin Ryu, and W. Song, "NeuroSpector: Dataflow and Mapping Optimizer for Deep Neural Network Accelerators" (2022)
• S. Jung, Y. Chon, J. Hwang, and W. Song, "RelSim: Computational Framework for Lifetime Reliability Modeling of Heterogeneous Accelerator Systems" (2022)
• B. Kim, S. Lee, C. Park, H. Kim, and W. Song, "Nebula: Lightweight Neural Network Benchmarks" (2020)
• W. Song, "Kite: Architecture Simulator for RISC‑V Instruction Set" (2019)