Λεπτομέρειες βιβλιογραφικής εγγραφής
| 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] |
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| Βάση Δεδομένων: |
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