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HiAER-spike software-hardware reconfigurable platform for event-driven neuromorphic computing at scale.

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Τίτλος: HiAER-spike software-hardware reconfigurable platform for event-driven neuromorphic computing at scale.
Συγγραφείς: Frank, Gwenevere, Hota, Gopabandhu, Wang, Keli, Deng, Christopher, Arora, Krish, Vins, Diana, Uppal, Abhinav, Olajide, Omowuyi, Yoshimoto, Kenneth, Wang, Qingbo, Yamaoka, Mari, Leugering, Johannes, Deiss, Stephen, Gibb, Leif, Cauwenberghs, Gert
Πηγή: npj Unconventional Computing; 5/4/2026, Vol. 3 Issue 1, p1-8, 8p
Θεματικοί όροι: Adaptive computing systems, Event processing (Computer science), Artificial neural networks, Edge computing, Python programming language, Systems design, Benchmarking (Management), Parallel processing, Parallel programming
Περίληψη: In this work, we present HiAER-Spike, a modular, reconfigurable, event-driven neuromorphic computing platform designed to execute large spiking neural networks with up to 160 million neurons and 40 billion synapses - roughly twice the neurons of a mouse brain at faster than real time. This system, assembled at the UC San Diego Supercomputer Center, comprises a co-designed hard- and software stack that is optimized for run-time massively parallel processing and hierarchical address-event routing (HiAER) of spikes while promoting memory-efficient network storage and execution. The architecture efficiently handles both sparse connectivity and sparse activity for robust and low-latency event-driven inference for both edge and cloud computing. A Python programming interface to HiAER-Spike, agnostic to hardware-level detail, shields the user from complexity in the configuration and execution of general spiking neural networks with minimal constraints in topology. The system is made easily available over a web portal for use by the wider community. In the following, we provide an overview of the hard- and software stack, explain the underlying design principles, demonstrate some of the system's capabilities, and solicit feedback from the broader neuromorphic community. Examples are shown demonstrating HiAER-Spike's capabilities for event-driven vision on benchmark CIFAR-10, DVS event-based gesture, MNIST, and Pong tasks. [ABSTRACT FROM AUTHOR]
Copyright of npj Unconventional Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: HiAER-spike software-hardware reconfigurable platform for event-driven neuromorphic computing at scale.
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  Data: In this work, we present HiAER-Spike, a modular, reconfigurable, event-driven neuromorphic computing platform designed to execute large spiking neural networks with up to 160 million neurons and 40 billion synapses - roughly twice the neurons of a mouse brain at faster than real time. This system, assembled at the UC San Diego Supercomputer Center, comprises a co-designed hard- and software stack that is optimized for run-time massively parallel processing and hierarchical address-event routing (HiAER) of spikes while promoting memory-efficient network storage and execution. The architecture efficiently handles both sparse connectivity and sparse activity for robust and low-latency event-driven inference for both edge and cloud computing. A Python programming interface to HiAER-Spike, agnostic to hardware-level detail, shields the user from complexity in the configuration and execution of general spiking neural networks with minimal constraints in topology. The system is made easily available over a web portal for use by the wider community. In the following, we provide an overview of the hard- and software stack, explain the underlying design principles, demonstrate some of the system's capabilities, and solicit feedback from the broader neuromorphic community. Examples are shown demonstrating HiAER-Spike's capabilities for event-driven vision on benchmark CIFAR-10, DVS event-based gesture, MNIST, and Pong tasks. [ABSTRACT FROM AUTHOR]
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  Label:
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  Data: <i>Copyright of npj Unconventional Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1038/s44335-026-00062-8
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        Text: English
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