Machine-learning (ML) techniques can optimize core operating system paths—scheduling, I/O, power, and memory—yet practical deployments remain rare. Existing prototypes either (i) bake simple heuristics directly into the kernel or (ii) off-load inference to user space to exploit discrete accelerators, both of which incur unacceptable engineering or latency cost. We argue that eBPF, the Linux kernel’s safe, hot-swappable byte-code runtime, is the missing substrate for moderately complex in-kernel ML. We present eBPFML, a design that (1) extends the eBPF instruction set with matrix-multiply helpers, (2) leverages upcoming CPU matrix engines such as Intel Advanced Matrix Extensions (AMX) through the eBPF JIT, and (3) retains verifier guarantees and CO-RE portability.
@inproceedings{10.1145/3748355.3748363,author={Sodhi, Prabhpreet Singh and Liargkovas, Georgios and Kaffes, Kostis},title={Empowering machine-learning assisted kernel decisions with eBPFML},year={2025},isbn={9798400720840},publisher={Association for Computing Machinery},address={New York, NY, USA},url={https://doi.org/10.1145/3748355.3748363},doi={10.1145/3748355.3748363},journal={Proceedings of the 3rd Workshop on EBPF and Kernel Extensions},pages={28–30},numpages={3},keywords={Operating systems, eBPF, hardware acceleration, machine learning},location={Coimbra, Portugal},series={eBPF '25},}
2023
HotOS ’23
Executing Shell Scripts in the Wrong Order, Correctly
@inproceedings{LKGV23,author={Liargkovas, Georgios and Kallas, Konstantinos and Greenberg, Michael and Vasilakis, Nikos},journal={The 19th Workshop on Hot Topics in Operating Systems},title={Executing Shell Scripts in the Wrong Order, Correctly},year={2023},doi={10.1145/3593856.3595891},}
arxiv
Quieting the Static: A Study of Static Analysis Alert Suppressions
@article{liargkovas2023quieting,title={Quieting the Static: A Study of Static Analysis Alert Suppressions},author={Liargkovas, Georgios and Panourgia, Evangelia and Spinellis, Diomidis},journal={arXiv preprint},year={2023},doi={https://doi.org/10.48550/arXiv.2311.07482},}
2022
TE ’22
Software Engineering Education Knowledge Versus Industrial Needs
@article{9612087,author={Liargkovas, Georgios and Papadopoulou, Angeliki and Kotti, Zoe and Spinellis, Diomidis},journal={IEEE Transactions on Education},title={Software Engineering Education Knowledge Versus Industrial Needs},year={2022},volume={65},number={3},pages={419-427},doi={10.1109/TE.2021.3123889},}
