가속화지능형시스템연구실

With no doubt the most popular accelerator for AI nowadays is GPU. However the world is heading towards the next step: AI-specific accelerators. There is much room to improve in terms of accelerator designs. For example, optimizing dataflow, utilizing sparse network structure, or processing-in-memory techniques. To utilize multiple devices (i.e., GPUs) for high-speed DNN training, it’s common to employ distributed learning. There are still many ways to improve current distributed learning methods: Devising a new communication algorithm, smartly pipelining the jobs, or changing the ways that devices synchronize. Efficient deployment and training of deep neural networks require reducing both computation and memory overhead. Our research focuses on advanced quantization and compression techniques to accelerate both inference and training across a wide range of models, including large language models (LLMs), diffusion-based image generation, graph neural networks, and spiking neural networks. We aim to enable fast and resource-efficient learning and inference on various hardware platforms without significant loss of accuracy. 석박사 신입생 및 학부생 인턴을 상시 선발하고 있습니다. 관심있는 학생은 leejinho at snu dot ac dot kr 로 연락 바랍니다.