Aug 2026· International Journal of Computer Network and Information Security· 0 citations
TL;DR
Findings indicate that the ACORISCVbSNN model has the potential to advance the field of bio-inspired computing, providing a highly accurate, energyefficient, and low-latency system for real-world use.
Abstract
Neuromorphic computing is a paradigm based on the computational mechanisms of the human brain and hasreceived considerable attention as a real-time technique with low energy requirements. Present systems, however, arelimited in their ability to scale traditional processors to a neuromorphic architecture, leading to issues with latency,power consumption, and smooth data flow. To address these problems, this paper proposes the ACORISC-VbSNNframework, comprising a modular RISC-V architecture, Spiking Neural Networks (SNNs), and Ant ColonyOptimization (ACO). The system uses a shared-memory architecture to maximize communication between traditionaland neuromorphic processors, ensuring data is managed effectively. The postulated framework processes the sensorydata by pre-processing and encoding them using rate coding, and dynamically optimizing memory access. SNNs arealso used to process spike trains in real-time, whereas ACO is used to determine the best data paths to minimizebottlenecks. Experimental analysis shows that the system performs better, with ultra-low power consumption of 0.0095mW, very low latency of 0.000544 seconds, and 99.2 percent accuracy. These findings indicate that the ACORISCVbSNN model has the potential to advance the field of bio-inspired computing, providing a highly accurate, energyefficient, and low-latency system for real-world use.
It is aimed at proving that SNNs have potential in such areas as computer vision, robotics, and speech recognition, and their role in overcoming the barrier between artificial and biological neural systems is proved.
Mesala Sravani, K. Kumari, S. M. Reddy· International Journal of Unc...· 0 citations
The view taken here is that brain-inspired computing is heading toward a hybrid future: conventional digital processors will keep doing what they do best, while event-driven and in-memory accelerators take over the workloads where they have a genuine edge.
Jisna C. Jeejo, Habeeba M. A.· International Journal of Tec...· 0 citations
- 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.
Manjula Biradar· Iconic research and engineer...· 0 citations
The exponential growth of Artificial Intelligence (AI) applications has created unprecedented demands for computational power, energy efficiency, and real-time data processing. Conventional von Neumann computing architectures suffer from significant limitations, including high power consumption, memory bottlenecks, and inefficient execution of brain-inspired algorithms. Neuromorphic computing has emerged as a promising paradigm that mimics the structure and functionality of biological neural systems to achieve highly efficient information processing. Neuromorphic chips integrate neuron-inspired processing elements, synaptic networks, event-driven communication mechanisms, and adaptive learning capabilities into specialized hardware platforms. This paper presents an advanced neuromorphic chip design framework that combines spiking neural networks, memristive synapses, asynchronous processing, and low-power VLSI techniques to develop an energy-efficient intelligent computing architecture. The proposed system aims to emulate biological neural behavior while reducing computational complexity and power consumption. Through event-driven processing and distributed memory computation, the architecture achieves superior performance for machine learning, edge AI, robotics, and cognitive computing applications. Experimental evaluation demonstrates significant improvements in energy efficiency, processing latency, and scalability compared to conventional AI hardware architectures. The proposed framework highlights the potential of neuromorphic engineering in shaping future intelligent systems capable of adaptive and autonomous learning.
Keywords— Neuromorphic Computing, Neuromorphic Chips, Spiking Neural Networks, Memristors, Artificial Intelligence Hardware, VLSI Design, Brain-Inspired Computing, Edge AI.
T. O. K. T Om Kumar, Thotla Indupriya Thotla Indupriya, G. N. G Nagarjuna· International Journal of Cre...· 0 citations
This work proposes heterogeneous neural networks that combine spiking neural networks (SNNs) and artificial neural networks (ANNs) at bandwidth-limited regions, such as chip boundaries, where spike-based communication reduces data transfer overhead.
Joshua Nardone, Rui-Jie Zhu, Ruhai Lin et al.· International Conference on...· 0 citations
A high-performance hardware implementation of large-scale neuromorphic system to investigate anesthetic-induced neural dynamics and paves the way for advanced brain–machine interfaces and closed-loop anesthetic delivery systems.
Chuan-Guang Wang, Xiaotian Pan, Si Chen et al.· Frontiers in Systems Neurosc...· 0 citations
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