{
  "id": "oai:papers.qnfo.org:jpcub-leading-indicator",
  "slug": "jpcub-leading-indicator",
  "title": "JPCUB as a Leading Indicator of Computing Paradigm Shifts: Retrospective Validation and Prospective Forecast",
  "authors": [
    "Rowan Brad Quni-Gudzinas"
  ],
  "abstract": "The computing industry lacks a single, cross-domain metric that can compare energy efficiency across fundamentally different paradigms — CPU, GPU, neuromorphic processor, in-memory accelerator, or spintronic logic. Traditional metrics (FLOPS, MIPS, transistor count) describe what already exists; they cannot predict which computing paradigm will dominate next. This paper proposes JPCUB (Joules per Computational Unit of Benefit) as an energy-centric metric for assessing paradigm transitions, validates it retrospectively against six historical computing transitions from vacuum tubes to AI accelerators, and applies it prospectively to seven post-silicon candidates. The retrospective identifies the conditions under which JPCUB provides advance warning of paradigm transitions: when energy efficiency is the binding constraint on computing progress (the post-Dennard era), JPCUB improvement precedes market dominance by 3-5 years; when energy is not the binding constraint (the vacuum tube and transistor eras), JPCUB provides no advance warning and lags adoption by 5-10 years. This conditional relationship — rather than an unconditional claim that JPCUB is a leading indicator in all circumstances — is the paper's central finding. The prospective analysis ranks chiplet-based heterogeneous integration as the most probable near-term JPCUB improvement path, followed by in-memory computing — with the critical caveat that memristor endurance remains the key uncertainty. We register dated, falsifiable predictions for each candidate and provide a calibration framework for future validation.",
  "abstract_markdown": "The computing industry lacks a single, cross-domain metric that can compare energy efficiency across fundamentally different paradigms — CPU, GPU, neuromorphic processor, in-memory accelerator, or spintronic logic. Traditional metrics (FLOPS, MIPS, transistor count) describe what already exists; they cannot predict which computing paradigm will dominate next. This paper proposes JPCUB (Joules per Computational Unit of Benefit) as an energy-centric metric for assessing paradigm transitions, validates it retrospectively against six historical computing transitions from vacuum tubes to AI accelerators, and applies it prospectively to seven post-silicon candidates. The retrospective identifies the conditions under which JPCUB provides advance warning of paradigm transitions: when energy efficiency is the binding constraint on computing progress (the post-Dennard era), JPCUB improvement precedes market dominance by 3-5 years; when energy is not the binding constraint (the vacuum tube and transistor eras), JPCUB provides no advance warning and lags adoption by 5-10 years. This conditional relationship — rather than an unconditional claim that JPCUB is a leading indicator in all circumstances — is the paper's central finding. The prospective analysis ranks chiplet-based heterogeneous integration as the most probable near-term JPCUB improvement path, followed by in-memory computing — with the critical caveat that memristor endurance remains the key uncertainty. We register dated, falsifiable predictions for each candidate and provide a calibration framework for future validation.",
  "date": "2026-08-02",
  "first_published": "2026-08-02",
  "last_revised": "2026-08-02T00:00:00Z",
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    "energy efficiency metrics",
    "computing paradigm shifts",
    "Koomey's law",
    "post-silicon computing",
    "benchmarking"
  ],
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  "url": "https://papers.qnfo.org/papers/jpcub-leading-indicator",
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        "name": "Rowan Brad Quni-Gudzinas",
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    "titles": [
      {
        "title": "JPCUB as a Leading Indicator of Computing Paradigm Shifts: Retrospective Validation and Prospective Forecast"
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        "subject": "energy efficiency metrics"
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      {
        "subject": "computing paradigm shifts"
      },
      {
        "subject": "Koomey's law"
      },
      {
        "subject": "post-silicon computing"
      },
      {
        "subject": "benchmarking"
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