RRepoGEO

REPOGEO REPORT · LITE

intel/intel-npu-acceleration-library

Default branch main · commit 073ad6a3 · scanned 5/31/2026, 9:51:25 PM

GitHub: 710 stars · 83 forks

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface intel/intel-npu-acceleration-library, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the project's purpose statement in the README

    Why:

    CURRENT
    The README currently starts with the EOL notice before the project title.
    COPY-PASTE FIX
    Move the '## PROJECT NOT UNDER ACTIVE MANAGEMENT' section to appear *after* a concise introductory sentence that describes the library's function, placed immediately following the '# Intel® NPU Acceleration Library' heading. For example:

# Intel® NPU Acceleration Library
The Intel® NPU Acceleration Library provided tools and optimizations for accelerating deep learning inference on Intel Neural Processing Units (NPUs).

## PROJECT NOT UNDER ACTIVE MANAGEMENT
This project will no longer be maintained by Intel.
...
  • mediumhomepage#2
    Add a homepage URL to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    https://github.com/intel/intel-npu-acceleration-library

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface intel/intel-npu-acceleration-library
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Core ML
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Core ML · recommended 2×
  2. TensorFlow Lite · recommended 1×
  3. TensorFlow Lite Converter · recommended 1×
  4. TensorFlow Lite Delegate API · recommended 1×
  5. Qualcomm Neural Processing SDK · recommended 1×
  • CATEGORY QUERY
    How can I accelerate deep learning inference performance on neural processing units?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. TensorFlow Lite Converter
    3. TensorFlow Lite Delegate API
    4. Qualcomm Neural Processing SDK
    5. MediaTek NeuroPilot SDK
    6. Samsung NPU SDK
    7. OpenVINO Toolkit
    8. Model Optimizer
    9. Inference Engine
    10. ONNX Runtime
    11. NVIDIA TensorRT
    12. Core ML

    AI recommended 12 alternatives but never named intel/intel-npu-acceleration-library. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What libraries optimize AI model execution for specialized deep learning accelerator hardware?
    you: not recommended
    AI recommended (in order):
    1. TensorRT
    2. OpenVINO (intel/openvino)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. Apache TVM (apache/tvm)
    5. XLA
    6. PyTorch Mobile / Lite Interpreter (pytorch/pytorch)
    7. Core ML

    AI recommended 7 alternatives but never named intel/intel-npu-acceleration-library. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of intel/intel-npu-acceleration-library?
    pass
    AI did not name intel/intel-npu-acceleration-library — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts intel/intel-npu-acceleration-library in production, what risks or prerequisites should they evaluate first?
    pass
    AI named intel/intel-npu-acceleration-library explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo intel/intel-npu-acceleration-library solve, and who is the primary audience?
    pass
    AI did not name intel/intel-npu-acceleration-library — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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intel/intel-npu-acceleration-library — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite