HiAER-Spike Software-Hardware Reconfigurable Platform for Event-Driven Neuromorphic Computing at Scale
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2026
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| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_18072 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HiAER-Spike Software-Hardware Reconfigurable Platform for Event-Driven Neuromorphic Computing at Scale 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 Hardware Architecture Artificial Intelligence 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. |
| title | HiAER-Spike Software-Hardware Reconfigurable Platform for Event-Driven Neuromorphic Computing at Scale |
| topic | Hardware Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2602.18072 |