让操作系统内核像大脑一样智能，实时响应自主系统需求。
Composable OS Kernel Architectures for Autonomous Intelligence
- 将内核模块变为AI计算单元，直接在内核空间处理感知与推理。
- 扩展Linux内核支持深度学习推理、浮点加速和自适应调度。
- 用范畴论统一符号推理与可微逻辑，构建神经符号内核架构。
随着智能系统渗透至边缘设备、云基础设施和嵌入式实时环境，本研究提出一种面向智能系统的新型操作系统内核架构，将内核从静态资源管理器转变为可自适应的AI集成平台。核心贡献包括：（1）将可加载内核模块（LKMs）作为面向AI的计算单元，在内核空间实现快速感知与认知处理；（2）将Linux内核拓展为原生支持深度学习推理、浮点加速和实时自适应调度的AI环境，高效执行机器学习工作负载；（3）引入基于范畴论与同伦类型论的神经符号内核设计，实现操作系统内部符号推理与可微逻辑的统一。上述方法共同使操作系统能够主动预判并适应自主智能应用的认知需求。
原文摘要 · Abstract (English)
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.
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