Machine Learning (ML) library in Linux kernel
2026-03-02 • Machine Learning
Machine LearningOperating Systems
AI summaryⓘ
The authors describe how adding machine learning (ML) inside the Linux kernel is tricky because the kernel doesn't support floating-point operations and ML could slow down the system. They propose a new way to include ML models in the kernel by creating a special infrastructure that works around these issues. To show it can work, they built a simple prototype that connects the kernel and user space for running ML models. This approach aims to help Linux become smarter without hurting performance.
Linux kernelmachine learningkernel spaceuser spacefloating-point operationsperformance degradationinfrastructure architecturemodel proxyproof of concept
Authors
Viacheslav Dubeyko
Abstract
Linux kernel is a huge code base with enormous number of subsystems and possible configuration options that results in unmanageable complexity of elaborating an efficient configuration. Machine Learning (ML) is approach/area of learning from data, finding patterns, and making predictions without implementing algorithms by developers that can introduce a self-evolving capability in Linux kernel. However, introduction of ML approaches in Linux kernel is not easy way because there is no direct use of floating-point operations (FPU) in kernel space and, potentially, ML models can be a reason of significant performance degradation in Linux kernel. Paper suggests the ML infrastructure architecture in Linux kernel that can solve the declared problem and introduce of employing ML models in kernel space. Suggested approach of kernel ML library has been implemented as Proof Of Concept (PoC) project with the goal to demonstrate feasibility of the suggestion and to design the interface of interaction the kernel-space ML model proxy and the ML model user-space thread.