What does this research mean for the field?

Neuromorphic computing is transitioning from small proof-of-concept chips to larger programmable platforms and is poised to become a key component in hybrid systems for low-power, real-time edge intelligence rather than displacing mainstream AI hardware outright. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To evaluate the current state of neuromorphic hardware, algorithms, and software ecosystems while examining key technical barriers and future deployment pathways.

August 24, 2026International Journal of Technology & Emerging ResearchOpen Access

Neuromorphic Computing: Current Progress and the Future of Brain-Inspired Computing

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Authors

JJJisna C Jeejo HAHabeeba M A

Discussion

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Overview

Review highlights progress in brain-inspired computing platforms and hardware, suggesting neuromorphic systems will complement digital processors in low-power edge applications.

Key Points

To evaluate the current state of neuromorphic hardware, algorithms, and software ecosystems while examining key technical barriers and future deployment pathways.
Reviewed recent literature on spiking neural networks, digital and analog architectures, and memristive device technologies.
Assessed software ecosystems, fabrication challenges, and application domains spanning robotics, biomedical monitoring, and edge intelligence.
Hardware development has progressed from small proof-of-concept chips to larger, programmable architectures featuring integrated algorithm co-design.
Persistent bottlenecks remain regarding training methodologies, benchmarking standards, device variability, and semiconductor fabrication constraints.
Neuromorphic systems are projected to integrate into hybrid computing frameworks alongside conventional processors to accelerate power-constrained edge workloads.

Cite This Study

Jeejo et al. (2026) studied this question.

synapsesocial.com/papers/6a8c009fbca056c88e6df730 https://doi.org/10.64823/ijter.2621029

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August 24, 2026International Journal of Technology & Emerging ResearchOpen Access

Neuromorphic Computing: Current Progress and the Future of Brain-Inspired Computing

View Full Paper

Ask AI

Bookmark

Authors

JJJisna C Jeejo HAHabeeba M A

Discussion

Member takes

Overview

Review highlights progress in brain-inspired computing platforms and hardware, suggesting neuromorphic systems will complement digital processors in low-power edge applications.

Key Points

To evaluate the current state of neuromorphic hardware, algorithms, and software ecosystems while examining key technical barriers and future deployment pathways.
Reviewed recent literature on spiking neural networks, digital and analog architectures, and memristive device technologies.
Assessed software ecosystems, fabrication challenges, and application domains spanning robotics, biomedical monitoring, and edge intelligence.
Hardware development has progressed from small proof-of-concept chips to larger, programmable architectures featuring integrated algorithm co-design.
Persistent bottlenecks remain regarding training methodologies, benchmarking standards, device variability, and semiconductor fabrication constraints.
Neuromorphic systems are projected to integrate into hybrid computing frameworks alongside conventional processors to accelerate power-constrained edge workloads.

Cite This Study

Jeejo et al. (2026) studied this question.

synapsesocial.com/papers/6a8c009fbca056c88e6df730 https://doi.org/10.64823/ijter.2621029

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