Neuromorphic Computing: Bridging Biological Intelligence and Artificial Intelligence
Authors/Creators
- 1. Department of Computer Engineering, Narayana Educational Institution, Andhra Pradesh, India.
Description
Abstract: Neuromorphic computing represents a groundbreaking paradigm shift in the realm of artificial intelligence, aiming to replicate the architecture and operational mechanisms of the human brain. This paper provides a comprehensive exploration of the foundational principles that underpin this innovative approach, examining the technological implementations that are driving advancements in the field. We delve into a diverse array of applications across various sectors, highlighting the versatility and relevance of neuromorphic systems. Key challenges such as scalability, integration with existing technologies, and the complexity of accurately modeling intricate brain functions are thoroughly analyzed. The discussion includes potential solutions and future prospects, illuminating pathways to overcome these obstacles. To illustrate the tangible impact of these technologies, we present practical examples that underscore their transformative potential in domains such as robotics, where they enable adaptive learning and autonomy; healthcare, where they enhance diagnostic tools and personalized medicine; cognitive computing, which facilitates improved human-computer interaction; and the development of smart cities, optimizing urban infrastructure and resource management. Through this examination, the paper aims to underscore the significance of neuromorphic computing in shaping the future of intelligent systems and fostering a deeper understanding of both artificial and natural intelligence.
Files
B455814021224.pdf
Files
(709.5 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:d498b4d61b9c3b2af7562f9e1ec638c3
|
709.5 kB | Preview Download |
Additional details
Identifiers
- DOI
- 10.35940/ijeat.B4558.14021224
- EISSN
- 2249-8958
Dates
- Accepted
-
2024-12-15Manuscript received on 24 September 2024 | Revised Manuscript received on 28 September 2024 | Manuscript Accepted on 15 December 2024 | Manuscript published on 30 December 2024.
References
- Schuman, C D (2017). The State of Neuromorphic Computing: A Survey of the Current Landscape. IEEE Transactions on Neural Networks and Learning Systems, 28(11), 2947-2961. DOI: https://www.doi.org/10.48550/arXiv.1705.06963
- Furber, S. (2016). Large-Scale Brain Simulation: The SpiNNaker Project. Proceedings of the IEEE, 104(1), 152-163. DOI: https://www.doi.org/10.1109/JPROC.2014.2304638
- Fig. 1. Prasanna Date: Opportunities for neuromorphic computing algorithms and applications. Research gate. DOI:10.1038/s43588-021- 00184-y
- Fig. 2. Yoeri Van de Burgt: Organic materials and devices for braininspired computing: From artificial implementation to biophysical realism. Research gate. DOI: 10.1557/mrs.2020.194
- Fig. 3. T. Nathan Mundhenk, TrueNorth Ecosystem for Brain-Inspired Computing: Scalable Systems, Software, and Applications. Research Gate. DOI: 10.1109/SC.2016.11.
- Wikichip: Loihi-Intel, https://en.wikichip.org/wiki/intel/loihi.
- Hasan Erdem Yantır, Towards Efficient Neuromorphic Hardware: Unsupervised Adaptive Neuron Pruning. Research Gate. DOI: 10.3390/electronics9071059.
- Sheikh, Z., & Khetade, V. (2019). Modeling and Simulation of Asynchrony in Neuromorphic Computing. In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 9, pp. 676–685). https://doi.org/10.35940/ijitee.i7747.078919
- Magapu, H., Krishna Sai, M. R., & Goteti, B. (2024). Human Deep Neural Networks with Artificial Intelligence and Mathematical Formulas. In International Journal of Emerging Science and Engineering (Vol. 12, Issue 4, pp. 1–2). https://doi.org/10.35940/ijese.c9803.12040324
- Mukherjee, P., Palan, P., & Bonde, M. V. (2021). Using Machine Learning and Artificial Intelligence Principles to Implement a Wealth Management System. In International Journal of Soft Computing and Engineering (Vol. 10, Issue 5, pp. 26–31). https://doi.org/10.35940/ijsce.f3500.0510521
- Priyatharshini, Dr. R., Ram. A.S, A., Sundar, R. S., & Nirmal, G. N. (2019). Real-Time Object Recognition using Region based Convolution Neural Network and Recursive Neural Network. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 4, pp. 2813–2818). https://doi.org/10.35940/ijrte.d8326.118419
- Anilkumar B, P.Rajesh Kumar, Classification of MR Brain tumors with Deep Plain and Residual Feed forward CNNs through Transfer learning. (2019). In International Journal of Engineering and Advanced Technology (Vol. 8, Issue 6, pp. 1758–1763). https://doi.org/10.35940/ijeat.f8437.088619