Blackboard Mixture of Experts: A Unique Edge Architecture for Multi-Agent Neural Networks

The paper introduces a resource-efficient edge architecture designed to bring advanced multi-agent capabilities and spatial awareness to resource-constrained microcontrollers (e.g., ARM Cortex-M, RP2040) without the heavy compute and memory overhead of traditional floating-point models.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-20
DOI
https://doi.org/10.5281/zenodo.22049637
Primary Topic
Mobile Crowdsensing and Crowdsourcing
Type
article
Field-Weighted Citation Impact
0.00
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article

Blackboard Mixture of Experts: A Unique Edge Architecture for Multi-Agent Neural Networks

Oliverio Colon
Zenodo (CERN European Organization for Nuclear Research)
Mobile Crowdsensing and Crowdsourcing
article

Blackboard Mixture of Experts: A Unique Edge Architecture for Multi-Agent Neural Networks

Oliverio Colon
article en

Abstract

The paper introduces a resource-efficient edge architecture designed to bring advanced multi-agent capabilities and spatial awareness to resource-constrained microcontrollers (e.g., ARM Cortex-M, RP2040) without the heavy compute and memory overhead of traditional floating-point models.

Zenodo (CERN European Organization for Nuclear Research)
Decent work and economic growth
Openalex Percentile: Top 5%
Mobile Crowdsensing and Crowdsourcing
DOIOpen Access PDFOpenAlex
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Blackboard Mixture of Experts: A Unique Edge Architecture for Multi-Agent Neural Networks — Oliverio Colon · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS