Neuromorphic Computing with a Paradigm Shift in Energy-Efficient and Scalable AI Hardware for Real-Time Applications

S. Ismail, Laith; Al-Hiyali, Mohammed Isam; Pardaz Zhumabaevna, Kozhobekova; Khalil, Ibraheem Mohammed; Fadhel Mahdi, Akram

doi:10.22034/jipm.2025.728122

Neuromorphic Computing with a Paradigm Shift in Energy-Efficient and Scalable AI Hardware for Real-Time Applications

Document Type : Original Article

Authors

Laith S. Ismail ¹

Mohammed Isam Al-Hiyali ²

Kozhobekova Pardaz Zhumabaevna ³

Ibraheem Mohammed Khalil ⁴

Akram Fadhel Mahdi ⁵

¹ Al-Turath University, Baghdad 10013, Iraq

² Al-Mansour University College, Baghdad 10067, Iraq

³ Osh State University, Osh City 723500, Kyrgyzstan

⁴ Al-Rafidain University College Baghdad 10064, Iraq

⁵ Madenat Alelem University College, Baghdad 10006, Iraq

10.22034/jipm.2025.728122

Abstract

ABSTRACT
Background: Neuromorphic computing is a newly developed technology that is based on data-flow architectures similar to the brain, which has the potential to power energy-constrained, latency-sensitive, and large-scale applications. The lack of flexibility in energy consumption and response time of traditional systems is a problem where neuromorphic platforms shine in real-time applications like robotics, IoT and autonomous systems.
Objective: The article aims to assess the capabilities of neuromorphic computing platforms with respect to conventional schemes, both quantitatively and qualitatively, in terms of energy consumption, response time, modularity, and application-dependent adaptability, and to determine the drawbacks and application prospects for its further development.
Methods: The study uses a comparative analysis approach to compare the identified factors and make statistical comparisons of the performance measures. The performance of the neuromorphic platforms as compared to non-neuromorphic platforms like Intel Loihi, IBM TrueNorth, NVIDIA Tesla V100, and Google TPU is compared based on its applications in robotics, IoT, and especially in healthcare. Data is derived from the experimental assessments of knowledge and theoretical paradigms encountered in prior research studies.
Results: Neuromorphic systems showed better energy consumption, system size, and delay characteristics. Nevertheless, that the algorithm so excellently solves particular tasks does not mean that it can successfully be used regardless of its purpose, or can be adapted freely to new, further-reaching trends, such as quantum computing. Regression results demonstrate a high degree of dependency between these measures as well as their potential for real time data processing.
Conclusion: Neuromorphic computing can be regarded as a new paradigm of energy-efficient and scalable AI and is especially promising for latency-sensitive deployment. Their shortcomings have been discussed earlier, yet it is worth stating that extension of these approaches by hybrid systems and more sophisticated integration frameworks might open new opportunities and eventually promote them as a foundation for new-generation computation models.

Keywords

KEYWORDS: Neuromorphic computing

AI hardware

spiking neural networks (SNNs)

brain-inspired architecture

Loihi

TrueNorth

energy efficiency

real-time processing

edge computing

scalable AI systems

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