AIS-SNU/HILOS: Artifact Evaluation Submission

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Authors: Hongsun Jang, Jaeyong Song, Changmin Shin, Si Ung Noh, Jung Jaewon, Jisung Park, Jinho Lee
Cited by: 0

Abstract

HILOS: A Cost-Effective Near-Storage Processing Solution for Offline Inference of Long-Context LLMs Abstract This artifact provides the implementation of HILOS, a high-throughput near-storage processing system designed for batched LLM inference. We provide the complete source code to facilitate the reproduction of our experimental results. The artifact supports (1) the synthesis of FPGA binaries and (2) the deployment of the full inference pipeline. For detailed arguments, refer to the README file included in the artifact. Citation If you use this code in your research, please cite the following paper: ```bibtex@inproceedings{hilos, title={HILOS: A Cost-Effective Near-Storage Processing Solution for Offline Inference of Long-Context LLMs}, author={Jang, Hongsun and Song, Jaeyong and Shin, Changmin and Noh, Si Ung and Jung, Jaewon and Park, Jisung and Lee, Jinho}, booktitle={Proceedings of the 31st ACM International Conference on Architectural Support for Programming Languages and Operating Systems}, year={2026}}```

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
6 January 2026