JooHyoung Cha

Present: Master’s Program at UST-ETRI

Interest: Compiler, Embeded System, Optimization

Other Skill: Reverse Engineering, Infrastructure(OpenStack, K8s), Digital Twin

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news

Jan 15, 2016	A simple inline announcement with Markdown emoji!
Nov 07, 2015	A long announcement with details
Oct 22, 2015	A simple inline announcement.

latest posts

Feb 10, 2025	위노그라드 알고리즘 정리
Nov 05, 2024	외부에서 내부망으로 접근하기 위한 3가지의 방법론
Oct 20, 2024	GIST Developers' Night 2024에 연사로 참가하고 나서

selected publications

NeurIPS W.
ML^2Tuner: Efficient Code Tuning via Multi-Level Machine Learning Models

JooHyoung Cha, Munyoung Lee, Jinse Kwon, and 3 more authors

In Machine Learning for Systems Workshop at NeurIPS, Dec 2024

Abs DOI Bib HTML PDF

The increasing complexity of deep learning models necessitates specialized hardware and software optimizations, particularly for deep learning accelerators. Existing autotuning methods often suffer from prolonged tuning times due to profiling invalid configurations, which can cause runtime errors. We introduce ML^2Tuner, a multi-level machine learning tuning technique that enhances autotuning efficiency by incorporating a validity prediction model to filter out invalid configurations and an advanced performance prediction model utilizing hidden features from the compilation process. Experimental results on an extended VTA accelerator demonstrate that ML^2Tuner achieves equivalent performance improvements using only 12.3% of the samples required with a similar approach as TVM and reduces invalid profiling attempts by an average of 60.8%, Highlighting its potential to enhance autotuning performance by filtering out invalid configurations
@inproceedings{cha2024ml2tunerefficientcodetuning, title = {{ML$^2$}Tuner: Efficient Code Tuning via Multi-Level Machine Learning Models}, author = {Cha, JooHyoung and Lee, Munyoung and Kwon, Jinse and Lee, Jubin and Lee, Jemin and Kwon, Yongin}, eprint = {2411.10764}, archiveprefix = {arXiv}, primaryclass = {cs.LG}, booktitle = {Machine Learning for Systems Workshop at NeurIPS}, url = {https://arxiv.org/abs/2411.10764}, doi = {10.48550/arXiv.2411.10764}, year = {2024}, month = dec, pages = {1--12} }