What question did this study set out to answer?

To review the architectural, hardware, and algorithmic advances in neuromorphic computing developed between 2019 and 2024.

August 16, 2026Iconic Research and Engineering JournalsOpen Access

Neuromorphic Computing

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Authors

MBManjula BiradarGulbarga University

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Implication

Literature review reveals advances in brain-inspired hardware and spiking neural networks, highlighting pathways to overcome conventional computing bottlenecks.

Key Points

To review the architectural, hardware, and algorithmic advances in neuromorphic computing developed between 2019 and 2024.
Surveyed hardware platforms, including digital neuromorphic chips, memristors, spintronic circuits, photonics, and two-dimensional material devices.
Synthesized algorithmic progress in spiking neural networks, specifically temporal coding, surrogate gradient training, and biologically plausible learning rules.
Demonstrated that co-locating distributed memory and computational elements overcomes the Von Neumann bottleneck with vastly lower power consumption than conventional electronics.
Identified critical applications in brain–machine interfaces, neural prosthetics, and artificial general intelligence alongside ongoing trade-offs between precision and power.
Established that hardware-algorithm co-development is necessary to successfully integrate neuromorphic accelerators into existing computing architectures.

Cite This Study

Manjula Biradar (2026) studied this question.

synapsesocial.com/papers/6a817a91f2fb91fc834ae821 https://doi.org/10.64388/irev10i2-1722321

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Implication

Literature review reveals advances in brain-inspired hardware and spiking neural networks, highlighting pathways to overcome conventional computing bottlenecks.

Neuromorphic Computing

Discussion

Member takes

Implication

Key Points

Cite This Study

Neuromorphic Computing

Discussion

Member takes

Implication

Key Points

Cite This Study

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