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Neuromorphic Computing Architectures for Ultra-Low-Power Intelligent Embedded Systems

Rabindranath Thakur

Abstract

Neuromorphic computing represents a paradigm shift in computational architecture, inspired by the structure and functionality of biological neural systems. Unlike traditional von Neumann architectures, neuromorphic systems integrate memory and computation, enabling massively parallel, event-driven processing with exceptional energy efficiency. This makes them particularly well-suited for ultra-low-power intelligent embedded systems, where constraints on energy, latency, and form factor limit the applicability of conventional artificial intelligence solutions. This paper explores neuromorphic computing architectures with a focus on their relevance to intelligent embedded systems operating at the edge. It examines core architectural principles, neuron and synapse models, and hardware implementations, including digital, analog, and mixed-signal designs. A comprehensive literature review highlights recent advancements, challenges, and comparative evaluations of neuromorphic platforms such as spiking neural networks and brain-inspired processors. The research methodology proposes a systematic approach for designing, modeling, and evaluating neuromorphic architectures tailored to ultra-low-power embedded environments. Performance metrics such as energy efficiency, latency, scalability, and adaptability are emphasized. The paper aims to provide a foundational understanding and methodological framework to support future research and development of energy-efficient neuromorphic systems for embedded intelligence in applications such as Internet of Things devices, autonomous sensors, biomedical implants, and edge AI platforms

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 9 No. 1 (2026): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2026.0901001

Pages

1-9

Published

January 15, 2026
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Rabindranath Thakur (%2026). Neuromorphic Computing Architectures for Ultra-Low-Power Intelligent Embedded Systems. International Journal of Science, Research and Technology , Vol. 9 No. 1 (2026): International Journal of Science, Research and Technology (IJSRAT) , pp. 1-9. https://doi.org/10.15662/IJSRAT.2026.0901001

References

1. Mead, C. (1990). Neuromorphic electronic systems. Proceedings of the IEEE, 78(10), 1629–1636.
2. Indiveri, G., & Liu, S. C. (2015). Memory and information processing in neuromorphic systems. Proceedings of the IEEE, 103(8), 1379–1397.
3. Furber, S. (2016). Large-scale neuromorphic computing systems. Journal of Neural Engineering, 13(5), 051001.
4. Merolla, P. A., Arthur, J. V., Alvarez-Icaza, R., Cassidy, A. S., Sawada, J., Akopyan, F., … & Modha, D. S. (2014). A million spiking-neuron integrated circuit with a scalable communication network (TrueNorth). Science, 345(6197), 668–673.
5. Davies, M., et al. (2018). Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro, 38(1), 82–99.
6. Schuman, C. D., et al. (2017). A survey of neuromorphic computing and neural networks in hardware. arXiv:1705.06963.
7. Qiao, N., et al. (2015). A reconfigurable on-chip learning platform for spiking neural networks. Frontiers in Neuroscience, 9, 141.
8. Benjamin, B. V., et al. (2014). Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations. Proceedings of the IEEE, 102(5), 699–716.
9. Pfeiffer, M., & Pfeil, T. (2018). Deep learning with spiking neurons: Opportunities and challenges. Frontiers in Neuroscience, 12, 774.
10. Chicca, E., & Indiveri, G. (2020). Neuromorphic engineering: From neural systems to brain-like engineered systems. Nature Reviews Physics, 2(5), 209–220.
11. Zhang, W., et al. (2020). Efficient neuromorphic computing architectures for edge AI. IEEE Transactions on Computers, 69(9), 1369–1382.
12. Roy, K., Jaiswal, A., & Panda, P. (2019). Towards spike-based machine intelligence with neuromorphic computing. Nature, 575(7784), 607–617.
13. Tang, J., et al. (2021). Neuromorphic vision sensors and computing for low-power perception. IEEE Access, 9, 11345–11359.
14. Wang, Y., et al. (2022). Memristive devices for neuromorphic computing: From synapses to systems. Advanced Materials, 34(7), 2107421.
15. Lee, J., et al. (2023). Low-power neuromorphic processors for embedded systems. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 13(2), 281–295.
16. Han, J., et al. (2024). Architectural innovations in spiking neural networks for energy-efficient edge intelligence. ACM Computing Surveys, 56(4), 89.
17. Kumar, A., et al. (2023). Neuromorphic computing for real-time robotics: Challenges and opportunities. Robotics and Autonomous Systems, 164, 104399.
18. Smith, S. T., & Gupta, R. (2025). Benchmarking frameworks for neuromorphic computing platforms. IEEE Transactions on Neural Networks and Learning Systems.
19. Zhao, Q., & Li, H. (2024). Adaptive learning mechanisms in neuromorphic systems for embedded applications. Journal of Artificial Intelligence Research, 71, 345–372.
20. Patel, M., et al. (2025). Neuromorphic architectures in healthcare monitoring devices: A survey. IEEE Reviews in Biomedical Engineering.