Composable OS Kernel Architectures for Autonomous Intelligence

• URL: http://arxiv.org/abs/2508.00604v1
• Date: Fri, 01 Aug 2025 13:07:16 GMT
• Title: Composable OS Kernel Architectures for Autonomous Intelligence
• Authors: Rajpreet Singh, Vidhi Kothari,
• Abstract summary: This research proposes a new OS kernel architecture for intelligent systems, transforming kernels from static resource managers to AI-integrated platforms.<n>Key contributions include treating Loadable Kernel Modules (LKMs) as AI-oriented 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• Accelerating Machine Learning Systems via Category Theory: Applications to Spherical Attention for Gene Regulatory Networks [0.0]
  We use neural circuit diagrams to prove a general theorem related to deep learning algorithms.<n>We guide the development of a novel attention algorithm catered to the domain of gene regulatory networks.<n>Our kernel achieves comparable performance to the state-of-the-art, fine-tuned FlashAttention algorithm.
  arXiv  Detail & Related papers  (2025-05-14T12:24:22Z)
• Neural Brain: A Neuroscience-inspired Framework for Embodied Agents [58.58177409853298]
  Current AI systems, such as large language models, remain disembodied, unable to physically engage with the world.<n>At the core of this challenge lies the concept of Neural Brain, a central intelligence system designed to drive embodied agents with human-like adaptability.<n>This paper introduces a unified framework for the Neural Brain of embodied agents, addressing two fundamental challenges.
  arXiv  Detail & Related papers  (2025-05-12T15:05:34Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [59.52058740470727]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• Analog Alchemy: Neural Computation with In-Memory Inference, Learning and Routing [0.08965418284317034]
  I explore an alternative way with memristive devices for neural computation, where the unique physical dynamics of the devices are used for inference, learning and routing.<n>I will provide hardware evidence of adaptability of local learning to memristive substrates, new material stacks and circuit blocks that aid in solving the credit assignment problem and efficient routing between analog crossbars for scalable architectures.
  arXiv  Detail & Related papers  (2024-12-30T10:35:03Z)
• Cognitive Kernel: An Open-source Agent System towards Generalist Autopilots [54.55088169443828]
  We introduce Cognitive Kernel, an open-source agent system towards the goal of generalist autopilots.<n>Unlike copilot systems, which primarily rely on users to provide essential state information, autopilot systems must complete tasks independently.<n>To achieve this, an autopilot system should be capable of understanding user intents, actively gathering necessary information from various real-world sources, and making wise decisions.
  arXiv  Detail & Related papers  (2024-09-16T13:39:05Z)
• SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence [51.6943465041708]
  Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency. We contribute a full-stack toolkit for pre-processing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips.
  arXiv  Detail & Related papers  (2023-10-25T13:15:17Z)
• Towards a Dynamic Composability Approach for using Heterogeneous Systems in Remote Sensing [0.0]
  We present a novel approach for using composable systems in the intersection between scientific computing, artificial intelligence (AI), and remote sensing domain. We describe the architecture of a first working example of a composable infrastructure that federates Expanse, an NSF-funded supercomputer, with Nautilus, a geo-distributed cluster.
  arXiv  Detail & Related papers  (2022-11-13T14:48:00Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)
• Towards automated kernel selection in machine learning systems: A SYCL case study [0.0]
  We present initial results using machine learning to select kernels in a case study deploying high performance SYCL kernels in libraries. By combining auto-tuning and machine learning these kernel selection processes can be deployed with little developer effort to achieve high performance on new hardware.
  arXiv  Detail & Related papers  (2020-03-15T11:23:36Z)
• PolyScientist: Automatic Loop Transformations Combined with Microkernels for Optimization of Deep Learning Primitives [55.79741270235602]
  We develop a hybrid solution to the development of deep learning kernels. We use the advanced polyhedral technology to automatically tune the outer loops for performance.
  arXiv  Detail & Related papers  (2020-02-06T08:02:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.