Accelerated Computing Systems Lab (ACSys) is affiliated with CS, Yonsei University. We conduct research on system and architectural issues for accelerating various applications such as deep learning, compression algorithms and graph processing.
ACSys Lab is currently looking for talented students (graduate students, undergraduate interns).
firstname.lastname@example.org iamjinholee at gmail dot com if you are interested.
석박사 신입생 및 학부생 인턴을 상시 선발하고 있습니다. 관심있는 학생은 iamjinholee at gmail dot com 으로 연락 바랍니다.
Mar. 2022: Our CVPR 2022 paper 'AIT' has been selected for an oral presentation (342/8161 = 4.2%). Double congratulations!
Mar. 2022: Jinho Lee received Yonsei Best Teaching Award for 2021.
Mar. 2022: Our paper titled GCoM: A Detailed GPU Core Model for Accurate Analytical Modeling of Modern GPUs has been accepted to ISCA 2022. See you in New York Ü
Mar. 2022: Our paper It's All In the Teacher: Zero-shot Quantization Brought Closer to the Teacher has been accepted at CVPR 2022. Congratulations!
Feb. 2022: We have two newly accepted papers on Valentine's day. Congratulations authors Ü
Jan. 2022: Our paper SALoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs has been accepted at IPDPS 2022.
Hope we get to travel to France :)It's going virtual.
Jan. 2022: SeongYeon Park joins the Lab. Welcome!
Oct. 2021: Jaewon Jung joins the Lab. Welcome!
Sep. 2021: SeongYeon Park became the first place winner for ACM Student Research Competition (SRC) at PACT 2021. Nice work!
Sep. 2021: Our paper Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples has been accepted at NeurIPS 2021. Congratulations!
Jul. 2021: Jaeyong Song and Hyeyoon Lee join the Lab. Welcome on board!
May. 2021: Our paper Making a Better Use of Caches for GCN Accelerators with Feature Slicing and Automatic Tile Morphing has been accepted at IEEE CAL. Congratulations!
May. 2021: Our paper AutoReCon: Neural Architecture Search-based Reconstruction for Data-free Compression has been accepted at IJCAI 2021.
Mar. 2021: Jinho Lee received Yonsei Best Teaching Award for 2020.
Feb. 2021: We have two papers accepted to DAC 2021. Congratulations authors!
Feb. 2021: Mingi Yoo joins the Lab. Welcome!
Oct. 2020: Our paper GradPIM: A Practical Processing-in-DRAM Architecture for Gradient Descent has been accepted at HPCA 2021
Jul. 2020: Deokki Hong and Kanghyun Choi join the Lab. Welcome!
Jul. 2020: Our paper FlexReduce: Flexible All-reduce for Distributed Deep Learning on Asymmetric Network Topology is published at DAC 2020
We conduct research on system and architectural issues for accelerating various applications such as deep learning, compression algorithms and graph processing, especially on FPGAs and GPUs. Some of the on-going research topics are listed below. However, you're free to bring your own exciting topic.
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.
Multiple model compression techniques have been suggested these days to reduce the computation burden from the nature of DNNs. Most of them utilize original training data to compensate for accuracy losses. Otherwise, they may end up with significant accuracy degradation. However, the original training data is usually inaccessible due to privacy or copyright issues. To this end, our research focuses on compressing neural networks while maintaining comparable performance, even without the original dataset.