RRepoGEO

REPOGEO REPORT · LITE

fengbintu/Neural-Networks-on-Silicon

Default branch master · commit 96364c63 · scanned 5/21/2026, 9:23:12 PM

GitHub: 2,091 stars · 393 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 fengbintu/Neural-Networks-on-Silicon, 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

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

OVERALL DIRECTION
  • highreadme#1
    Clarify repo's purpose as a curated paper collection in README's opening

    Why:

    CURRENT
    Fengbin Tu is an Assistant Professor and the Associate Director of the Institute of Integrated Circuits and Systems at The Hong Kong University of Science and Technology, NSFC Excellent Young Scientist, and a core faculty member of the AI Chip Center for Emerging Smart Systems (ACCESS) under InnoHK. For more informantion about Dr. Tu, please refer to his homepage. Dr. Tu's main research interest is AI chip and system. This is an exciting field where fresh ideas come out every day, so he's collecting works on related topics. Welcome to join!
    COPY-PASTE FIX
    This repository is a curated collection of academic papers and research on neural network accelerators, deep learning, and computer architecture, maintained by Dr. Fengbin Tu. It serves as a selection of key works in the exciting field of AI chips and systems. Welcome to explore!
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, specifying the terms under which the collection and its original content are shared. Consider a permissive license like MIT or Apache 2.0 if appropriate for a collection of links/metadata.
  • mediumhomepage#3
    Add Dr. Tu's homepage to the repository's 'Homepage' field

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    Add Dr. Tu's official homepage URL to the repository's 'Homepage' field in the GitHub 'About' section.

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 fengbintu/Neural-Networks-on-Silicon
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ASPLOS
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ASPLOS · recommended 2×
  2. ISCA · recommended 2×
  3. MICRO · recommended 2×
  4. HPCA · recommended 2×
  5. Google Scholar · recommended 1×
  • CATEGORY QUERY
    Where can I find academic papers on deep learning hardware acceleration?
    you: not recommended
    AI recommended (in order):
    1. Google Scholar
    2. arXiv
    3. ACM Digital Library
    4. IEEE Xplore Digital Library
    5. ResearchGate
    6. NeurIPS
    7. ICLR
    8. MLSys
    9. ASPLOS
    10. ISCA
    11. MICRO
    12. HPCA
    13. DAC
    14. Hot Chips

    AI recommended 14 alternatives but never named fengbintu/Neural-Networks-on-Silicon. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What resources explore the intersection of deep learning algorithms and specialized hardware architectures?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Developer Blog & Documentation
    2. CUDA
    3. cuDNN
    4. TensorRT
    5. Google AI Blog & TensorFlow Documentation
    6. TensorFlow (tensorflow/tensorflow)
    7. Deep Learning Systems: Algorithms, Compilers, and Hardware for AI
    8. Computer Architecture: A Quantitative Approach
    9. ISCA
    10. MICRO
    11. HPCA
    12. ASPLOS
    13. MIT 6.S082: Engineering for Deep Learning
    14. Graphcore Documentation & Blog

    AI recommended 14 alternatives but never named fengbintu/Neural-Networks-on-Silicon. 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 fengbintu/Neural-Networks-on-Silicon?
    pass
    AI did not name fengbintu/Neural-Networks-on-Silicon — 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 fengbintu/Neural-Networks-on-Silicon in production, what risks or prerequisites should they evaluate first?
    pass
    AI named fengbintu/Neural-Networks-on-Silicon 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 fengbintu/Neural-Networks-on-Silicon solve, and who is the primary audience?
    pass
    AI did not name fengbintu/Neural-Networks-on-Silicon — 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?

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fengbintu/Neural-Networks-on-Silicon — 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