# PolyForm Noncommercial License 1.0.0

https://polyformproject.org/licenses/noncommercial/1.0.0

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## Copyright License

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==================================================
STATEMENT OF PRIOR ART AND ADDITIONAL LICENSE TERMS
Aligned with PolyForm Noncommercial License 1.0.0
==================================================

Author: Andres Sebastian Pirolo
ORCID:  0009-0004-3899-1222
Email:  andrespirolo@gmail.com

DOI: https://doi.org/10.5281/zenodo.19840566

Title: Sub-Millisecond Exact Topological Pruning for Edge AI:
       A Konig Bipartite Approach with SIMD Acceleration

Date of First Publication: April 27, 2026

==================================================

1. PERMITTED USE

Academic research, independent study, education, benchmarking, validation,
reproducibility studies, and experimental evaluation is permitted under the
terms of the PolyForm Noncommercial License 1.0.0, provided proper
attribution is given.

==================================================

2. STATEMENT OF PRIOR ART

This document constitutes a formal public disclosure establishing prior art
for the following work:

  Title:    Sub-Millisecond Exact Topological Pruning for Edge AI:
            A Konig Bipartite Approach with SIMD Acceleration

  Files:    konig_pruning.pdf  (paper)
            konig_pruning.tex  (LaTeX source)
            fig_pipeline.png   (pipeline diagram)
            fig_speed.pdf      (speed comparison figure)
            fig_pruning.pdf    (pruning quality figure)

  Language: C++ (ARM NEON SIMD), Python (PyTorch validation)
  Target architecture: any (hardware-agnostic algorithm)

  Specific contributions disclosed herein:

  (a) Exact structural pruning of neural network Fully Connected layers
      via Minimum Vertex Cover on bipartite graphs, solved in polynomial
      time via Konig's Theorem and the Hopcroft-Karp algorithm.

  (b) SIMD-vectorized adjacency matrix construction achieving 9.2x
      speedup over scalar implementation, without scalar fallback.

  (c) Shannon-stratified population sampling (stride S=3) to prevent
      trivial MVC collapse on asymmetric dense networks.

  (d) End-to-end pipeline: C++ binary mask export -> PyTorch injection,
      validated on MobileNetV2 (1.28M weights, ImageNet pre-trained).

  (e) Empirical demonstration that the pruned model (84.6% compression,
      0.301 ms) outperforms the full MobileNetV2 baseline on limousine
      classification (p=0.000204, Bayesian analysis).

==================================================

3. SCOPE OF THE LICENSE

All implementations of the methods described herein -- whether in software
or hardware -- are governed exclusively by the PolyForm Noncommercial
License 1.0.0.

This includes implementations in any programming language and on any
programmable or dedicated hardware platform, including but not limited to
CPU, GPU, FPGA, ASIC, or similar architectures.

License applicability is determined by functional equivalence rather than
textual similarity. The license applies to reimplementations in any
programming language, mathematically equivalent formulations producing
substantially similar behavior, and any system reproducing the same
Konig-based MVC pruning, SIMD adjacency construction, or
Shannon-stratified sampling mechanisms.

There is no de minimis exception. Extraction, refactoring, translation,
paraphrasing, or reimplementation after exposure constitutes derivative use.

Any individual or entity that has reviewed, studied, tested, or otherwise
been exposed to the materials in this record shall be considered
knowledge-contaminated. Subsequent implementations are not considered
clean-room unless supported by contemporaneous, auditable evidence of
prior independent development.

==================================================

4. NONCOMMERCIAL RESTRICTION

Any use of this work that falls outside the definition of noncommercial use,
as defined by the PolyForm Noncommercial License 1.0.0, is not permitted
under this license.

==================================================

5. COMMERCIAL AND OPERATIONAL USE

Any commercial, corporate, governmental, financial, military, or
revenue-generating use -- in whole or in part -- requires explicit written
authorization from the author. This includes integration into commercial
AI inference software (in any language), proprietary edge or cloud
deployments, hardware and firmware implementations, or FPGA/ASIC designs
implementing the disclosed algorithm.

Commercial licensing contact: andrespirolo@gmail.com

==================================================

6. DERIVATIVE WORKS AND FUNCTIONAL EQUIVALENCE

Reimplementation, translation, refactoring, architectural modification,
optimization, or claims of functional equivalence do not remove a work from
the scope of the PolyForm Noncommercial License 1.0.0.

6.a. CODE SIZE IRRELEVANCE (NO MINIMUM THRESHOLD)

The applicability of the PolyForm Noncommercial License 1.0.0 is independent
of code size, number of lines, percentage of implementation, degree of
literal similarity, or partial extraction. There is no minimum threshold.

==================================================

7. KNOWLEDGE CONTAMINATION AND ATTRIBUTION

Exposure to this work constitutes prior knowledge of the disclosed methods.
Subsequent implementations making material use of the disclosed concepts
remain subject to attribution and license requirements.

==================================================

8. INTELLECTUAL PROPERTY OWNERSHIP

All intellectual property rights remain with the author, subject only to
the permissions explicitly granted under the PolyForm Noncommercial
License 1.0.0.

==================================================

9. NO ADDITIONAL RIGHTS GRANTED

This document does not grant any rights beyond those expressly provided by
the PolyForm Noncommercial License 1.0.0.

==================================================

10. ENFORCEMENT AND REMEDIES

Any use in violation of the PolyForm Noncommercial License 1.0.0 shall be
addressed through the remedies provided by that license and by applicable
international law.

==================================================

11. VERSIONING

Updates to presentation, terminology, benchmarks, or documentation do not
alter the core methodological contributions, the scope of protection, or
the license terms. The most recent license statement is authoritative.

==================================================

12. KNOWLEDGE CONTAMINATION

Any individual or entity that has reviewed, studied, tested, or otherwise
been exposed to the materials in this record shall be considered
knowledge-contaminated. Subsequent implementations are not considered
clean-room unless supported by contemporaneous, auditable evidence of
prior independent development.

==================================================

(c) 2026 Andres Sebastian Pirolo. All rights reserved.
Buenos Aires, Argentina
ORCID: 0009-0004-3899-1222
andrespirolo@gmail.com

