{
  "abstract": "Modern computing is commonly organized around computational resources, including central processing units (CPUs), graphics processing units (GPUs), memory, storage, and network infrastructure. Users and developers select these resources, configure software, deploy algorithms, and manage execution in order to obtain desired results. However, the ultimate objective of most computational activities is not to possess or operate a particular processing device. It is to obtain a result that satisfies a computational goal. This paper introduces Goal-Oriented Intelligent Computing (GOIC), a conceptual framework that treats computation as an outcome-oriented service rather than a resource-centered activity. Under this framework, a user specifies a desired result, together with relevant quality, cost, latency, reliability, privacy, and other constraints. An intelligent computing instrument then determines how to achieve that result by autonomously selecting computational methods, allocating resources, decomposing tasks, coordinating heterogeneous hardware, evaluating intermediate outputs, and adapting execution when necessary. A GPU may remain an important component of the underlying infrastructure, but it is no longer necessarily the primary object that users must select, configure, or manage. The paper develops the conceptual foundations, system architecture, formal optimization model, operating cycle, implementation strategy, potential applications, and evaluation methodology of this approach. It distinguishes the proposed framework from existing GPU programming, cloud computing, serverless execution, and intent-driven orchestration by emphasizing the user-facing abstraction of computational outcomes and the autonomous management of the complete process required to produce them. This is a conceptual framework rather than an empirically validated system. Its contribution is a research agenda for transforming computing from a resource-access paradigm into a goal-oriented, adaptive, and outcome-centered paradigm.\n\nKeywords: goal-oriented computing; intelligent computing instrument;",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Jincheng Zhang",
      "orcid": "0009-0005-1860-0009"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/aw757tqan0kdf.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23591781",
  "fulltext_url": null,
  "id": "aw757tqan0kdf",
  "issns": [],
  "landing_url": "https://zenodo.org/records/23591781",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": "https://zenodo.org/api/records/23591781/files/Beyond%20the%20GPU%20Goal-Oriented%20Intelligent%20Computing%20as%20an%20Autonomous%20Instrument%20for%20Computational%20Outcomes.pdf/content",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/aw757tqan0kdf.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T12:21:17Z",
      "native_id": "23591781",
      "source": "zenodo",
      "url": "https://zenodo.org/records/23591781"
    }
  ],
  "subjects": [],
  "title": "Beyond the GPU: Goal-Oriented Intelligent Computing as an Autonomous Instrument for Computational Outcomes",
  "title_text": "Beyond the GPU: Goal-Oriented Intelligent Computing as an Autonomous Instrument for Computational Outcomes",
  "type": "preprint",
  "updated_at": "2026-11-06T12:21:17Z",
  "url": "https://research.draconitic.com/paper/aw757tqan0kdf",
  "venue": "Zenodo",
  "versions": []
}