https://deep-learning-profiling-tools.github.io/CAT-Lab/tag/sigcse/SIGCSEHugo Blox Builder (https://hugoblox.com)en-usMon, 14 Sep 2026 00:00:00 +0000https://deep-learning-profiling-tools.github.io/CAT-Lab/media/icon_hu6808975029018430273.pnghttps://deep-learning-profiling-tools.github.io/CAT-Lab/tag/sigcse/https://deep-learning-profiling-tools.github.io/CAT-Lab/news/2026-09-14-nkilab-sigcse2027/Mon, 14 Sep 2026 00:00:00 +0000https://deep-learning-profiling-tools.github.io/CAT-Lab/news/2026-09-14-nkilab-sigcse2027/<p>Our paper <a href="https://deep-learning-profiling-tools.github.io/CAT-Lab/CAT-Lab/publication/nkilab_sigcse2027/"><em>NKI Teaching Lab: An Accelerator-Free Toolchain for Learning Tile-Level AI Accelerator Programming</em></a>, by <strong>Jialiang Gu, Junjie Ma, Thai-Hoa Nguyen, Hao Wu, Zhicong Lu, Shuyin Jiao, Binqian Yin, and Keren Zhou</strong>, has been accepted at <strong>SIGCSE TS 2027</strong>, the ACM Technical Symposium on Computer Science Education. The teaching lab helps students learn tile-level AI accelerator programming without accelerator hardware. Congratulations to all the authors!</p>