Energy-Efficient Neuromorphic Chips for Real-Time Robotic Control: A Review
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Energy-Efficient Neuromorphic Chips for Real-Time Robotic Control: A Review

Shuming Liu 1*
1 University of Birmingham
*Corresponding author: liushuming653@gmail.com
Published on 3 September 2025
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TNS Vol.134
ISSN (Print): 2753-8826
ISSN (Online): 2753-8818
ISBN (Print): 978-1-80590-343-7
ISBN (Online): 978-1-80590-344-4
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Abstract

Neuromorphic computing has gained increasing attention as a bio-inspired solution to the limitations of traditional computing systems in power and real-time constraints. In robotic control tasks, real-time processing and energy efficiency are crucial, especially for autonomous and mobile systems. Neuromorphic chips mimic the structure and operation of biological neurons, enabling low-latency and low-power processing. This review examines the architectures, performance benchmarks, and application cases of mainstream neuromorphic hardware platforms used in robotic perception and control. It also compare their energy consumption and latency with conventional control platforms. Furthermore, this paper identifies key challenges in system integration and suggests future directions including improved scalability, online learning, and the combination with edge AI frameworks. The paper finds that neuromorphic chips can significantly reduce energy consumption and improve real-time performance in robotic control. However, challenges in algorithm adaptation, standardization, and hardware integration remain. This study aims to provide researchers with insights into the practical implementation of neuromorphic control systems in energy-sensitive robotic applications.

Keywords:

Neuromorphic computing, Spiking neural network, Real-time control, Energy efficiency, Robotics

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Liu,S. (2025). Energy-Efficient Neuromorphic Chips for Real-Time Robotic Control: A Review. Theoretical and Natural Science,134,73-78.

References

[1]. Davies, M., Srinivasa, N., Lin, T. H., Chinya, G., Cao, Y., Choday, S. H., Dimou, G., Joshi, P., Imam, N., Jain, S., & Liao, Y. (2018). Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro, 38(1), 82–99. doi: 10.1109/MM.2018.112130359.

[2]. Furber, S. B., Galluppi, F., Temple, S., & Plana, L. A. (2014). The SpiNNaker project. Proceedings of the IEEE, 102(5), 652–665. doi: 10.1109/JPROC.2014.2304638.

[3]. Aitsam, M., Di Nuovo, A., & Ahmad, S. (2022). Neuromorphic computing for interactive robotics: A systematic review. IEEE Access, 10, 113952–113970. doi: 10.1109/ACCESS.2022.3219440.

[4]. Schuman, C. D., Potok, T. E., Patton, R. M., Birdwell, J. D., Dean, M. E., Rose, G. S., & Plank, J. S. (2017). A survey of neuromorphic computing and neural networks in hardware. arXiv preprint. Available: https: //arxiv.org/abs/1705.06963

[5]. Yang, Y., Wang, M., et al. (2023). Neuromorphic electronics for robotic perception and navigation: Current trends and future outlook. Neurocomputing, 533. Available: https: //www.sciencedirect.com/science/article/abs/pii/S0952197623010229

[6]. Sandamirskaya, Y., Kaboli, M., Conradt, J., & Celikel, T. (2022). Neuromorphic computing hardware and neural architectures for robotics. Science Robotics, 7(67), eabl8419.

[7]. Glatz, S., Pedersen, B. U., Mayr, C., & Schmuker, M. (2018). Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor. arXiv preprint. Available: https: //arxiv.org/abs/1810.10801

[8]. Putra, R. V. W., Shibata, M., & Yamashita, Y. (2024). Embodied neuromorphic artificial intelligence for robotics: Perspectives, challenges, and research development stack. arXiv preprint. Available: https: //arxiv.org/abs/2404.03325

[9]. Merolla, P. A., Arthur, J. V., Alvarez-Icaza, R., Cassidy, A. S., Sawada, J., et al. (2014). A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 345(6197), 668–673. doi: 10.1126/science.1254642.

References

[1]. Davies, M., Srinivasa, N., Lin, T. H., Chinya, G., Cao, Y., Choday, S. H., Dimou, G., Joshi, P., Imam, N., Jain, S., & Liao, Y. (2018). Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro, 38(1), 82–99. doi: 10.1109/MM.2018.112130359.

[2]. Furber, S. B., Galluppi, F., Temple, S., & Plana, L. A. (2014). The SpiNNaker project. Proceedings of the IEEE, 102(5), 652–665. doi: 10.1109/JPROC.2014.2304638.

[3]. Aitsam, M., Di Nuovo, A., & Ahmad, S. (2022). Neuromorphic computing for interactive robotics: A systematic review. IEEE Access, 10, 113952–113970. doi: 10.1109/ACCESS.2022.3219440.

[4]. Schuman, C. D., Potok, T. E., Patton, R. M., Birdwell, J. D., Dean, M. E., Rose, G. S., & Plank, J. S. (2017). A survey of neuromorphic computing and neural networks in hardware. arXiv preprint. Available: https: //arxiv.org/abs/1705.06963

[5]. Yang, Y., Wang, M., et al. (2023). Neuromorphic electronics for robotic perception and navigation: Current trends and future outlook. Neurocomputing, 533. Available: https: //www.sciencedirect.com/science/article/abs/pii/S0952197623010229

[6]. Sandamirskaya, Y., Kaboli, M., Conradt, J., & Celikel, T. (2022). Neuromorphic computing hardware and neural architectures for robotics. Science Robotics, 7(67), eabl8419.

[7]. Glatz, S., Pedersen, B. U., Mayr, C., & Schmuker, M. (2018). Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor. arXiv preprint. Available: https: //arxiv.org/abs/1810.10801

[8]. Putra, R. V. W., Shibata, M., & Yamashita, Y. (2024). Embodied neuromorphic artificial intelligence for robotics: Perspectives, challenges, and research development stack. arXiv preprint. Available: https: //arxiv.org/abs/2404.03325

[9]. Merolla, P. A., Arthur, J. V., Alvarez-Icaza, R., Cassidy, A. S., Sawada, J., et al. (2014). A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 345(6197), 668–673. doi: 10.1126/science.1254642.

Cite this article

Liu,S. (2025). Energy-Efficient Neuromorphic Chips for Real-Time Robotic Control: A Review. Theoretical and Natural Science,134,73-78.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-APMM 2025 Symposium: Controlling Robotic Manipulator Using PWM Signals with Microcontrollers

ISBN: 978-1-80590-343-7(Print) / 978-1-80590-344-4(Online)
Editor: Marwan Omar, Mustafa Istanbullu
Conference website: https://2025.confapmm.org/adana.html
Conference date: 19 September 2025
Series: Theoretical and Natural Science
Volume number: Vol.134
ISSN: 2753-8818(Print) / 2753-8826(Online)

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