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Related Experiment Video

Updated: Jun 25, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

EdgeVolution: democratizing multi-objective neural architecture search and end-to-end deployment on microcontrollers.

René Groh¹, Stefan Dendorfer², Mateo Ávila Pava²

¹Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Bavaria, Germany. rene.groh@fau.de.

Communications Engineering

|June 23, 2026

Summary

EdgeVolution optimizes artificial neural networks for edge devices, enhancing performance and reproducibility. This platform simplifies deploying AI models on resource-constrained hardware, making edge AI more accessible.

Related Experiment Videos

Last Updated: Jun 25, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

Computer Science
Artificial Intelligence
Embedded Systems

Background:

Edge AI enables running artificial neural networks on resource-constrained devices like microcontrollers.
Optimizing and deploying neural networks on edge hardware is challenging due to a lack of specialized tools, impacting performance and reproducibility.

Purpose of the Study:

To present EdgeVolution, an end-to-end hardware-in-the-loop platform for optimizing and deploying neural networks on edge devices.
To address challenges in hardware-specific adaptation, reproducibility, and performance for AI on edge.

Main Methods:

EdgeVolution provides a hardware-in-the-loop platform for multi-objective optimization and neural architecture selection.
The platform facilitates direct deployment onto target edge hardware.
A generic and adaptable pipeline is utilized for tailored model creation.

Main Results:

Demonstrated the versatility of EdgeVolution through four diverse application use cases.
Showcased wide-ranging applicability of the platform across different scenarios.
Validated the improvement in accessibility, performance, and reproducibility for AI on edge devices.

Conclusions:

EdgeVolution enhances the creation and deployment of neural network models for edge devices.
The platform successfully tailors AI models to specific datasets, tasks, and hardware constraints.
EdgeVolution improves the overall efficiency and reliability of AI applications on edge hardware.