Composable OS Kernel Architectures for Autonomous Intelligence

TL;DR

This paper introduces a novel OS kernel architecture that integrates AI capabilities directly into the kernel, enabling autonomous, adaptive, and intelligent processing for edge, cloud, and embedded systems.

Contribution

It presents a new AI-native kernel design with loadable modules as computation units, deep learning integration, and a neurosymbolic framework based on advanced mathematical theories.

Findings

Enhanced kernel processing speed for sensory data

Integrated deep learning inference within the kernel

Unified symbolic and differentiable reasoning in OS internals

Abstract

As intelligent systems permeate edge devices, cloud infrastructure, and embedded real-time environments, this research proposes a new OS kernel architecture for intelligent systems, transforming kernels from static resource managers to adaptive, AI-integrated platforms. Key contributions include: (1) treating Loadable Kernel Modules (LKMs) as AI-oriented computation units for fast sensory and cognitive processing in kernel space; (2) expanding the Linux kernel into an AI-native environment with built-in deep learning inference, floating-point acceleration, and real-time adaptive scheduling for efficient ML workloads; and (3) introducing a Neurosymbolic kernel design leveraging Category Theory and Homotopy Type Theory to unify symbolic reasoning and differentiable logic within OS internals. Together, these approaches enable operating systems to proactively anticipate and adapt to the cognitive needs of autonomous intelligent applications.