Sangyeob and his team have developed a C-DNN processor that effectively processes object recognition workloads, achieving 51.3% higher energy efficiency compared to the previous state-of-the-art processor. Subsequently, they have applied C-DNN not only to image classification but also to other applications, and have developed the C-Transformer, which applies this technique to a Large Language Model (LLM). As a result, they demonstrate that the energy consumed in LLM can be reduced by 30% to 72% using the C-DNN technique, compared to the previous state-of-the-art processor. In this talk, we will introduce the processor developed for C-DNN and C-Transformer, and discuss how neuromorphic computing can be used in actual applications in the future.
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About the Speaker
Sangyeob Kim (Student Member, IEEE) received the B.S., M.S. and Ph.D. degrees from the School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea, in 2018, 2020 and 2023, respectively. He is currently a Post-Doctoral Associate with the KAIST. His current research interests include energy-efficient system-on-chip design, especially focused on deep neural network accelerators, neuromorphic hardware, and computing-in-memory accelerators.
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