Sandia Deploys SpiNNaker2 Neuromorphic System from SpiNNcloud
June 5, 2025
DRESDEN, Germany, June 5, 2025 —
SpiNNcloud today announced that
Sandia National Laboratories has deployed a SpiNNaker2-based neuromorphic computing system to explore energy-efficient architectures for artificial intelligence and national security applications.
SpiNNaker2 Installation at Sandia National Labs. Photo credit: Craig Fritz, Sandia National Labs.
Developed by SpiNNcloud and based on research led by Steve Furber, designer of the original ARM architecture, SpiNNaker2 uses a large number of low-power processors to simulate spiking neural networks and support AI workloads.
The deployment supports Sandia’s broader efforts to investigate alternative computing architectures that reduce energy consumption while advancing capabilities in areas such as nuclear deterrence and AI research.
“Although GPU-based systems can boost the efficiency of supercomputers by processing highly parallel and math-intensive workloads much faster than CPUs, brain-inspired systems, like the SpiNNaker2 system, offer an enticing alternative,” said Craig M. Vineyard, Ph.D., research scientist at Sandia National Laboratories. “The new system delivers both impressive performance and substantial efficiency gains concurrently to Sandia’s neuromorphic capabilities.”
The SpiNNaker2 system employs a highly parallel architecture consisting of 48 SpiNNaker2 chips per server board, each containing 152 Arm-based cores and specialized accelerators. This design enables efficient, event-driven computation, allowing the system to perform complex simulations at a lower energy profile compared to traditional GPU-based systems. Such energy efficiency is crucial for applications where power consumption and cooling are limiting factors.
“Our vision is to pioneer the future of artificial intelligence through brain-inspired supercomputer technology for next-generation defense and beyond,” said Hector A. Gonzalez, co-founder and CEO of SpiNNcloud. “The SpiNNaker2’s efficiency gains make it particularly well-suited for the demanding computational needs of national security applications. We’re thrilled to partner with Sandia on this venture, and to see the system being brought to life first-hand.”
Looking ahead, SpiNNcloud is supporting emerging generative AI algorithms by enabling more efficient machine learning through dynamic sparsity. Rather than relying on dense models that activate all neural pathways, dynamic sparsity selectively engages only relevant subsets of neurons based on the input.
This shift supports the development of new, more efficient AI architectures and offers a potential path to mitigating the growing energy demands associated with large-scale model training and inference.
DRESDEN, Germany, June 5, 2025 — SpiNNcloud today announced that Sandia National Laboratories has deployed a SpiNNaker2-based neuromorphic computing system to explore energy-efficient architectures for artificial intelligence and national security […]
www.hpcwire.com