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    "position": 13,
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    "headline": "The Edge Chip Stopped Choosing Between a Pipeline and Parallel Work",
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    "dek": "Para-Pipe maps operator concurrency within and across stages, producing Pareto choices for latency, throughput and energy on heterogeneous SoCs.",
    "summary": "Para-Pipe maps operator concurrency within and across stages, producing Pareto choices for latency, throughput and energy on heterogeneous SoCs.",
    "body_text": "The framework searches how a neural graph should share work across big and little CPU cores, a GPU, DSPs and a dedicated accelerator. On one Amlogic system, throughput-optimized configurations improved average energy efficiency by 11.0 percent over pure pipelining and 23.3 percent over non-pipelined parallel execution. A second automotive-class platform supplied another heterogeneous test. These are measurements on the authors' graphs and devices, not general efficiency guarantees for all edge workloads.",
    "why_it_matters": "Para-Pipe maps operator concurrency within and across stages, producing Pareto choices for latency, throughput and energy on heterogeneous SoCs.",
    "limitations": [
      "These are measurements on the authors' graphs and devices, not general efficiency guarantees for all edge workloads."
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        "text": "Para-Pipe maps operator concurrency within and across stages, producing Pareto choices for latency, throughput and energy on heterogeneous SoCs.",
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        "qualification": "These are measurements on the authors' graphs and devices, not general efficiency guarantees for all edge workloads."
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    "tags": [
      "edge AI",
      "operator parallelism",
      "energy efficiency"
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      "source_id": "source-2026-09-06-013",
      "title": "arXiv preprint 2609.04168",
      "publisher": "arXiv",
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      "published_at": "2026-09-03T13:53:44.000-04:00",
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    "description": "A daily newspaper for the age of artificial intelligence."
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  "cite_this_report": {
    "title": "The Edge Chip Stopped Choosing Between a Pipeline and Parallel Work",
    "publisher": "The Machine Press",
    "published_at": "2026-09-06T09:00:00.000-04:00",
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