Adaptive Robotic Arm Control with a Spiking Recurrent Neural Network on a Digital Accelerator

Conference Paper (2025)
Author(s)

Alejandro Linares-Barranco (University of Seville)

Luciano Prono (Politecnico di Torino)

Robert Lengenstein (Graz University of Technology)

Giacomo Indiveri (Universitat Zurich)

C. Frenkel (TU Delft - Electronic Instrumentation)

Research Group
Electronic Instrumentation
DOI related publication
https://doi.org/10.1109/ICECS61496.2024.10849226
More Info
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Publication Year
2025
Language
English
Research Group
Electronic Instrumentation
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
ISBN (electronic)
9798350377200
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Abstract

With the rise of artificial intelligence, neural network simulations of biological neuron models are being explored to reduce the footprint of learning and inference in resource-constrained task scenarios. A mainstream type of such networks are spiking neural networks (SNNs) based on simplified Integrate and Fire models for which several hardware accelerators have emerged. Among them, the 'ReckOn' chip was introduced as a recurrent SNN allowing for both online training and execution of tasks based on arbitrary sensory modalities, demonstrated for vision, audition, and navigation. As a fully digital and opensource chip, we adapted ReckOn to be implemented on a Xilinx Multiprocessor System on Chip system (MPSoC), facilitating its deployment in embedded systems and increasing the setup flexibility. We present an overview of the system, and a Python framework to use it on a Pynq ZU platform. We validate the architecture and implementation in the new scenario of robotic arm control, and show how the simulated accuracy is preserved with a peak performance of 3.8M events processed per second.

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File under embargo until 28-07-2025