Home / Current Issue / Paper 1722321
Neuromorphic Computing
Subject area: Science,Engineering and Technology · Area of research: Neural Networks
DOI: https://doi.org/10.64388/IREV10I2-1722321
Abstract
The neuromorphic computing is a process that mimics the processing of information at biological synapses. The physical mechanism of neuromorphic computing is based on the ion transport at device interface to achieve efficient transfer and computation of information at the same location [11,12], which is different from logic circuit in digital systems [13,14]. Besides, neuromorphic computing is characterized by distributed memory and computational elements [15]. The neuromorphic computing can realize high-efficiency massive parallel computing that can conquer the limitation of Von Neumann bottleneck. Neuromorphic computing is a paradigm of designing hardware and algorithms inspired by the brain’s architecture and principles, promising major gains in energy efficiency and new computing capabilities. This review provides a comprehensive overview of developments in neuromorphic computing from 2019 through 2024. We survey hardware advances – including digital neuromorphic chips (e.g. Intel Loihi, IBM TrueNorth, and SpiNNaker), emerging device technologies like memristors, spintronic circuits, photonic processors, and two-dimensional (2D) material-based devices – that enable brain-like computation with vastly lower power than conventional electronics. We also summarize algorithmic advances in spiking neural networks (SNNs), covering progress in temporal coding strategies, the introduction of surrogate gradient methods for training SNNs like deep networks, and biologically plausible learning rules such as e-prop for online learning in spiking systems. Furthermore, we discuss key opportunities and gaps: the potential of neuromorphic systems to approach aspects of human cognition or artificial general intelligence (AGI), applications in medicine (like brain–machine interfaces and neural prosthetics) and science, the trade-offs between power efficiency and computational precision, and challenges in integrating neuromorphic accelerators into existing computing ecosystems. We conclude by highlighting how co-development of hardware and algorithms is critical to fulfill the promise of neuromorphic computing, and by outlining open research directions on the path toward more brain-like, efficient computing architectures.
Keywords
abstract, introduction, working, algorithm
How to cite this paper
@article{1722321,
author = {Manjula Biradar},
title = {Neuromorphic Computing},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {2},
pages = {1490-1495},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722321.pdf},
abstract = {The neuromorphic computing is a process that mimics the processing of information at biological synapses. The physical mechanism of neuromorphic computing is based on the ion transport at device interface to achieve efficient transfer and computation of information at the same location [11,12], which is different from logic circuit in digital systems [13,14]. Besides, neuromorphic computing is characterized by distributed memory and computational elements [15]. The neuromorphic computing can realize high-efficiency massive parallel computing that can conquer the limitation of Von Neumann bottleneck. Neuromorphic computing is a paradigm of designing hardware and algorithms inspired by the brain’s architecture and principles, promising major gains in energy efficiency and new computing capabilities. This review provides a comprehensive overview of developments in neuromorphic computing from 2019 through 2024. We survey hardware advances – including digital neuromorphic chips (e.g. Intel Loihi, IBM TrueNorth, and SpiNNaker), emerging device technologies like memristors, spintronic circuits, photonic processors, and two-dimensional (2D) material-based devices – that enable brain-like computation with vastly lower power than conventional electronics. We also summarize algorithmic advances in spiking neural networks (SNNs), covering progress in temporal coding strategies, the introduction of surrogate gradient methods for training SNNs like deep networks, and biologically plausible learning rules such as e-prop for online learning in spiking systems. Furthermore, we discuss key opportunities and gaps: the potential of neuromorphic systems to approach aspects of human cognition or artificial general intelligence (AGI), applications in medicine (like brain–machine interfaces and neural prosthetics) and science, the trade-offs between power efficiency and computational precision, and challenges in integrating neuromorphic accelerators into existing computing ecosystems. We conclude by highlighting how co-development of hardware and algorithms is critical to fulfill the promise of neuromorphic computing, and by outlining open research directions on the path toward more brain-like, efficient computing architectures.},
keywords = {abstract, introduction, working, algorithm},
month = {August},
doi = {https://doi.org/10.64388/IREV10I2-1722321}
}