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Related Experiment Video

Updated: Oct 28, 2025

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

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Published on: March 2, 2015

Benchmarking Highly Parallel Hardware for Spiking Neural Networks in Robotics.

Lea Steffen¹, Robin Koch¹, Stefan Ulbrich¹

¹Interactive Diagnosis and Service Systems (IDS), Intelligent Systems and Production Engineering (ISPE), FZI Research Center for Information Technology, Karlsruhe, Germany.

Frontiers in Neuroscience

|July 16, 2021

Summary

This summary is machine-generated.

Brain-inspired Spiking Neural Networks (SNN) show promise for robotics. This study benchmarks SNN performance on various hardware, comparing simulation time, energy use, and path length for robotic applications.

Keywords:

benchmark neurorobotic parallel hardware architectures robotic motion control spiking neural networks

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Area of Science:

Robotics and Artificial Intelligence
Computational Neuroscience
Neuromorphic Engineering

Background:

Animal brains surpass machine performance in speed and efficiency.
Significant advancements in robotic vision, motion, and path planning have been achieved.
Spiking Neural Networks (SNNs) offer brain-inspired computational advantages for robotics.

Purpose of the Study:

To benchmark the performance of SNNs on different hardware platforms relevant to robotics.
To compare simulation time, energy consumption, and pathfinding accuracy for robotic use cases.
To evaluate the suitability of neuromorphic hardware and GPUs for SNN simulations in robotics.

Main Methods:

A neural Wavefront algorithm, representative of robotic path planning, was simulated using SNNs.
The SNN model was developed in the simulator-independent language PyNN.
Performance was evaluated across different backends: Nest (CPU), SpiNNaker (neuromorphic hardware), and GeNN (GPU).
GeNN's performance was further analyzed across different hardware deployments.

Main Results:

Comparative analysis of total simulation time, average energy consumption, and path length was conducted.
Performance differences between serial CPU, neuromorphic hardware, and GPU implementations were identified.
Variations in GeNN performance across different GPU hardware were investigated.

Conclusions:

Insights into the performance of parallel hardware solutions for SNNs in robotics were gained.
The study aims to guide the development of more efficient SNN implementations for robotic applications.
Understanding hardware performance is crucial for optimizing SNNs in real-world robotic systems.