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| Home > Publications database > Neuromorphic computing: Brain-inspired concepts, platforms, and their role in HPC |
| Home > Publications database > Neuromorphic computing: Brain-inspired concepts, platforms, and their role in HPC |
| Poster (After Call) | FZJ-2025-02959 |
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2025
Abstract: Neuromorphic computing, inspired by the brain’s structure and functionality, seeks to replicate the brain’s efficiency and adaptability in computational systems. At its core, neuromorphic computing relies on spiking neural networks (SNNs), where computation occurs through neurons that accumulate charge over time and fire spikes once a threshold is reached. These networks communicate asynchronously, transmitting information through discrete events, making them inherently suited for tasks that require energy-efficient, real-time, and dynamic processing. Unlike traditional artificial neural networks (ANNs), SNNs rely on the temporal dynamics of spikes and charge accumulation to process and communicate information.A wide variety of neuromorphic platforms, both digital and analog, enable experimentation with brain-inspired networks. Platforms such as SpiNNaker, TrueNorth, Loihi, and BrainScaleS have been pivotal in advancing neuromorphic research, but there is a growing range of systems—spanning from small-scale edge applications to large-scale supercomputing environments. These platforms allow researchers to explore diverse applications, from real-time robotics to large-scale computational tasks, leveraging the unique capabilities of SNNs for energy-efficient, parallel, and adaptive computation. While these platforms have been pivotal in advancing neuromorphic computing, their potential within high-performance computing (HPC) remains largely untapped and underexplored.This poster explores the emerging synergies between neuromorphic computing and HPC, focusing on how the unique characteristics of SNNs can be leveraged to enhance computational models and workflows. Neuromorphic systems offer substantial advantages in energy efficiency, parallel processing, and real-time learning, which are particularly beneficial for complex tasks like brain simulations, AI inference, and optimization problems. The ability of neuromorphic systems to adapt and learn from real-time data presents exciting opportunities for advancing both specialized and general-purpose HPC applications.Additionally, we examine the integration of neuromorphic computing within modular supercomputing architectures (MSA). By incorporating neuromorphic modules alongside traditional HPC clusters, these hybrid systems could address challenges in scalability, energy consumption, and computational flexibility. For example, neuromorphic systems could assist in the emulation of quantum algorithms, enhance neural network-based applications like drug discovery, and support the development of future quantum computing frameworks.Despite these promising applications, the full integration of neuromorphic computing into HPC remains a significant challenge. Advancements in hardware, software frameworks, and integration with existing HPC systems are critical for unlocking the full potential of these technologies. As the field progresses, overcoming these challenges will enable neuromorphic computing to drive innovations in energy-efficient, adaptive, and large-scale computations, offering transformative solutions for the future of high-performance computing.
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