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ARXIV:2605.06927 · ENERGY-AWARE COMPUTER VISION · SUBMITTED 11 MAY · 20:47 UTC · FRESHNESS STALE
ARXIV:2605.06927ENERGY-AWARE COMPUTER VISIONSUBMITTED 11 MAY · 20:47 UTCFRESHNESS STALETony Tran · Richie R. Suganda · Bin Hu · arXiv
XiYOLO is an energy-adaptive object detection framework that uses iterative architecture search and compound scaling to create energy-efficient models for edge devices.
Opportunity summary
Pain XiYOLO is an energy-adaptive object detection framework that uses iterative architecture search and compound scaling to create energy-efficient models for edge devices.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
XiYOLO is an energy-adaptive object detection framework that uses iterative architecture search and compound scaling to create energy-efficient models for edge devices. Existing energy-aware NAS methods often target limited deployment settings, while real energy…
Object detection on heterogeneous edge devices must satisfy strict energy, latency, and memory constraints while still providing reliable perception for downstream autonomy. Existing energy-aware NAS methods often target limited deployment settings, while real energy…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Experiments on PascalVOC, COCO, and real-device deployment show that XiYOLO achieves a stronger energy-accuracy tradeoff than YOLO baselines.
Energy-Aware Computer Vision moved forward this cycle; last verified May 2026. Public score 5.0/10.
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XiYOLO is an energy-adaptive object detection framework that uses iterative architecture search and compound scaling to create energy-efficient models for edge devices.
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Paper Pack
10.48550/arXiv.2605.06927XiYOLO is an energy-adaptive object detection framework that uses iterative architecture search and compound scaling to create energy-efficient models for edge devices.
Abstract
Object detection on heterogeneous edge devices must satisfy strict energy, latency, and memory constraints while still providing reliable perception for downstream autonomy. Existing energy-aware NAS methods often target limited deployment settings, while real energy remains difficult to optimize because it is highly device-dependent and costly to measure. We address these challenges with an energy-adaptive framework that combines an energy-aware XiResOFA search space, a two-stage energy estimator, and iterative search to identify a single energy-efficient base architecture. We then apply compound scaling to transform this base design into the XiYOLO family across deployment budgets, enabling interpretable accuracy-energy tradeoffs under sparse hardware measurements. Experiments on PascalVOC, COCO, and real-device deployment show that XiYOLO achieves a stronger energy-accuracy tradeoff than YOLO baselines. On PascalVOC, the medium XiYOLO model reaches 86.15 mAP50 while reducing energy relative to YOLOv12m by 20.6% on GPU and 35.9% on NPU. On COCO, XiYOLO reduces energy relative to YOLOv12 by up to 53.7% on GPU and 51.6% on NPU at the small scale. The proposed two-stage estimator also improves sample efficiency over a joint predictor under few-shot adaptation with only 2-20 target-device samples.
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Dimensions overall score 5.0
PROBLEM
XiYOLO is an energy-adaptive object detection framework that uses iterative architecture search and compound scaling to create energy-efficient models for edge devices. Existing energy-aware NAS methods often target limited deployment settings, while real energy remains difficul...
METHOD
Object detection on heterogeneous edge devices must satisfy strict energy, latency, and memory constraints while still providing reliable perception for downstream autonomy. Existing energy-aware NAS methods often target limited deployment settings, while real energy remains dif...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Experiments on PascalVOC, COCO, and real-device deployment show that XiYOLO achieves a stronger energy-accuracy tradeoff than YOLO baselines.
WHY NOW
Energy-Aware Computer Vision moved forward this cycle; last verified May 2026. Public score 5.0/10.
Abstract-backed public claims while anchored extraction refreshes.
XiYOLO is an energy-adaptive object detection framework that uses iterative architecture search and compound scaling to create energy-efficient models for edge devices. Existing energy-aware NAS methods often target limited deployment settings, while real energy remains difficult to optimize because it is highly device-dependent and costly to measure.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Object detection on heterogeneous edge devices must satisfy strict energy, latency, and memory constraints while still providing reliable perception for downstream autonomy. Existing energy-aware NAS methods often target limited deployment settings, while real energy remains difficult to optimize because it is highly device-dependent and costly to measure.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Experiments on PascalVOC, COCO, and real-device deployment show that XiYOLO achieves a stronger energy-accuracy tradeoff than YOLO baselines.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Energy-Aware Computer Vision moved forward this cycle; last verified May 2026. Public score 5.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
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XiYOLO is an energy-adaptive object detection framework that uses iterative architecture search and compound scaling to create energy-efficient models for edge devices.
Segment
Energy-Aware Computer Vision
Adoption evidence
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Commercial read
5.0/10 public viability
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reason
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proof status
unverified
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confidence low
next verification path
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stale
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passport absent
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Artifact maturity
GitHub and Hugging Face maturity payloads
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Technical feasibility
partial
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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missing
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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DEFENSIBILITY
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