Published June 6, 2026 | Version v1

Harmonic Participation Sieve for Electronic Network Design: Switch Fabric Reduction, Power Grid Routing, and Relay Network Optimization

Authors/Creators

Description

 

We apply the Harmonic Participation Sieve, a graph-theoretic edge ranking algorithm previously validated in 3D physics simulation and neural network pruning, to three electronic network design problems.

A breadth-first spanning tree guarantees connectivity, while a harmonic participation score ranks each connection by its structural importance.

Applied to a 16×16 switch fabric, the sieve removes 28.8% of switches while retaining 100% of available paths and maintaining an identical minimum hop count.

Applied to a 20×20 VLSI power grid, it identifies 56 critical copper traces for maximum-width routing, versus 397 redundant traces for minimum-width routing.

Applied to a 64-node relay network, it removes 30.6% of links while preserving maximum-throughput path capacity.

Algorithm licensed under PolyForm Noncommercial 1.0.0.

Notes

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IMPLEMENTATION AND LICENSING NOTICE

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Academic investigation, non-commercial

research, and citation are explicitly

welcomed under the terms of CC BY-NC 4.0

(paper text) and PolyForm Noncommercial

1.0.0 (algorithm and implementations).

 

Any commercial implementation of the

methods described herein — in any

programming language, hardware platform,

or system architecture — requires explicit

written authorization from the author.

This includes integration into commercial

products, revenue-generating services,

or industrial research pipelines.

 

The author reserves all intellectual

property rights herein, including the

right to seek patent protection.

 

A larger theoretical result underpinning

these methods is in preparation and not

disclosed in this publication.

 

Commercial licensing and partnership

inquiries: lctrnc1@gmail.com

© 2026 Andrés Sebastián Pirolo

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Additional details

Software

Repository URL
https://zenodo.org/uploads/20573427
Programming language
Python