Empowering machine-learning assisted kernel decisions with eBPFML

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.