2025/08/01 by Rajpreet Singh, Singh, Rajpreet, Kothari, Vidhi
Computer Science · Engineering · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Cognitive Computing and Networks #FOS: Computer and information sciences #Ferroelectric and Negative Capacitance Devices #Operating Systems (cs.OS)
paper · pdf · doi:10.48550/arxiv.2508.00604
openalex publication_date 2025/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.