Re: [LSF/MM/BPF TOPIC] Machine Learning (ML) library in Linux kernel

[Hillf Danton <hdanton@sina.com>]

On Fri, 6 Feb 2026 19:38:28 +0000 Viacheslav Dubeyko wrote:
> Hello,
> 
> Machine Learning (ML) is approach/area of learning from data,
> finding patterns, and making predictions without implementing algorithms
> by developers. The number of areas of ML applications is growing
> with every day. Generally speaking, ML can introduce a self-evolving and
> self-learning capability in Linux kernel. There are already research works
> and industry efforts to employ ML approaches for configuration and
> optimization the Linux kernel. However, introduction of ML approaches
> in Linux kernel is not so simple and straightforward way. There are multiple
> problems and unanswered questions on this road. First of all, any ML model
> requires the floating-point operations (FPU) for running. But there is
> no direct use of FPUs in kernel space. Also, ML model requires training phase
> that can be a reason of significant performance degradation of Linux kernel.
> Even inference phase could be problematic from the performance point of view
> on kernel side. The using of ML approaches in Linux kernel is inevitable step.
> But, how can we use ML approaches in Linux kernel? Which infrastructure
> do we need to adopt ML models in Linux kernel?
>
Given the short list, eevdf, slab, ext4, IP stack, usb bus and kvm, ML is not
needed before the second half in 2027, because it wastes minutes to make
either liver or pancreas intelligent. By intelligent I mean liver can edit
ppt in Russian. Perhaps Cerebellum is an exception.

Can you build bot to fix syzbot reports before 2028?