Evolution at the Edge: Real-Time Evolution for Neuromorphic Engine Control

Karan Patel; Ethan Maness; Tyler Nitzsche; Emma G. Brown; Brett Witherspoon; Aaron Young; Bryan Maldonado; Brian Kaul; James S. Plank; Catherine D. Schuman

doi:10.1109/NICE65350.2025.11065602

Evolution at the Edge: Real-Time Evolution for Neuromorphic Engine Control

Karan Patel
, Ethan Maness
, Tyler Nitzsche
, Emma G. Brown
, Brett Witherspoon
, Aaron Young
, Bryan Maldonado
, Brian Kaul
, James S. Plank
, Catherine D. Schuman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Neuromorphic computing systems are attractive for real-time control at the edge because of their low power operation, real-time processing capabilities and their potential ability to do online learning. In this work, we describe an approach for performing real-time evolution of spiking neural networks for neuromorphic systems at the edge called Neuromorphic Optimization using Dynamic Evolutionary Systems or NODES. We apply this approach to real-time combustion engine control and develop an engine-specific hardware platform for NODES called FireBox. We demonstrate how the real-time evolution approach works in simulation and the performance of networks trained in simulation on the physical engine.

Original language	English
Title of host publication	IEEE Neuro-Inspired Computational Elements, NICE 2025 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798331503024
DOIs	https://doi.org/10.1109/NICE65350.2025.11065602
State	Published - 2025
Event	12th Annual IEEE Neuro-Inspired Computational Elements, NICE 2025 - Heidelberg, Germany Duration: Mar 25 2025 → Mar 28 2025

Publication series

Name	IEEE Neuro-Inspired Computational Elements, NICE 2025 - Proceedings

Conference

Conference	12th Annual IEEE Neuro-Inspired Computational Elements, NICE 2025
Country/Territory	Germany
City	Heidelberg
Period	03/25/25 → 03/28/25

Funding

This work was supported by DOE EERE grant number DE-EE0009177 and the UT-Oak Ridge Innovation Institute seed program. Notice: This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://www.energy.gov/doepublic-access-plan).

Keywords

combustion engines
neuromorphic computing
online learning
real-time control

Access to Document

10.1109/NICE65350.2025.11065602

Cite this

Patel, K., Maness, E., Nitzsche, T., Brown, E. G., Witherspoon, B., Young, A., Maldonado, B., Kaul, B., Plank, J. S., & Schuman, C. D. (2025). Evolution at the Edge: Real-Time Evolution for Neuromorphic Engine Control. In IEEE Neuro-Inspired Computational Elements, NICE 2025 - Proceedings (IEEE Neuro-Inspired Computational Elements, NICE 2025 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NICE65350.2025.11065602

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title = "Evolution at the Edge: Real-Time Evolution for Neuromorphic Engine Control",

abstract = "Neuromorphic computing systems are attractive for real-time control at the edge because of their low power operation, real-time processing capabilities and their potential ability to do online learning. In this work, we describe an approach for performing real-time evolution of spiking neural networks for neuromorphic systems at the edge called Neuromorphic Optimization using Dynamic Evolutionary Systems or NODES. We apply this approach to real-time combustion engine control and develop an engine-specific hardware platform for NODES called FireBox. We demonstrate how the real-time evolution approach works in simulation and the performance of networks trained in simulation on the physical engine.",

keywords = "combustion engines, neuromorphic computing, online learning, real-time control",

author = "Karan Patel and Ethan Maness and Tyler Nitzsche and Brown, \{Emma G.\} and Brett Witherspoon and Aaron Young and Bryan Maldonado and Brian Kaul and Plank, \{James S.\} and Schuman, \{Catherine D.\}",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 12th Annual IEEE Neuro-Inspired Computational Elements, NICE 2025 ; Conference date: 25-03-2025 Through 28-03-2025",

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doi = "10.1109/NICE65350.2025.11065602",

language = "English",

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Patel, K, Maness, E, Nitzsche, T, Brown, EG, Witherspoon, B , Young, A , Maldonado, B , Kaul, B, Plank, JS & Schuman, CD 2025, Evolution at the Edge: Real-Time Evolution for Neuromorphic Engine Control. in IEEE Neuro-Inspired Computational Elements, NICE 2025 - Proceedings. IEEE Neuro-Inspired Computational Elements, NICE 2025 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 12th Annual IEEE Neuro-Inspired Computational Elements, NICE 2025, Heidelberg, Germany, 03/25/25. https://doi.org/10.1109/NICE65350.2025.11065602

Evolution at the Edge: Real-Time Evolution for Neuromorphic Engine Control. / Patel, Karan; Maness, Ethan; Nitzsche, Tyler et al.
IEEE Neuro-Inspired Computational Elements, NICE 2025 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2025. (IEEE Neuro-Inspired Computational Elements, NICE 2025 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Patel, Karan

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AU - Nitzsche, Tyler

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AU - Witherspoon, Brett

AU - Young, Aaron

AU - Maldonado, Bryan

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AU - Plank, James S.

AU - Schuman, Catherine D.

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N2 - Neuromorphic computing systems are attractive for real-time control at the edge because of their low power operation, real-time processing capabilities and their potential ability to do online learning. In this work, we describe an approach for performing real-time evolution of spiking neural networks for neuromorphic systems at the edge called Neuromorphic Optimization using Dynamic Evolutionary Systems or NODES. We apply this approach to real-time combustion engine control and develop an engine-specific hardware platform for NODES called FireBox. We demonstrate how the real-time evolution approach works in simulation and the performance of networks trained in simulation on the physical engine.

AB - Neuromorphic computing systems are attractive for real-time control at the edge because of their low power operation, real-time processing capabilities and their potential ability to do online learning. In this work, we describe an approach for performing real-time evolution of spiking neural networks for neuromorphic systems at the edge called Neuromorphic Optimization using Dynamic Evolutionary Systems or NODES. We apply this approach to real-time combustion engine control and develop an engine-specific hardware platform for NODES called FireBox. We demonstrate how the real-time evolution approach works in simulation and the performance of networks trained in simulation on the physical engine.

KW - combustion engines

KW - neuromorphic computing

KW - online learning

KW - real-time control

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BT - IEEE Neuro-Inspired Computational Elements, NICE 2025 - Proceedings

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ER -

Patel K, Maness E, Nitzsche T, Brown EG, Witherspoon B , Young A et al. Evolution at the Edge: Real-Time Evolution for Neuromorphic Engine Control. In IEEE Neuro-Inspired Computational Elements, NICE 2025 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2025. (IEEE Neuro-Inspired Computational Elements, NICE 2025 - Proceedings). doi: 10.1109/NICE65350.2025.11065602

Evolution at the Edge: Real-Time Evolution for Neuromorphic Engine Control

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