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EN
Chip Bridges Neuromorphic And Deep-Network Computing (TU Dresden)
['Technical Paper Link', 'Tom Katsioulas', 'Alex', 'Matt Bailey', 'Alexis R. Ware', 'Yağız Boz', 'Murugavel Ganesan', 'F. Chen', 'B.S. Deepaksubramanyan', 'Brian Bailey']
Semiconductor Engineering
Researchers from Technische Universität Dresden and University of Manchester published a technical paper titled “The SpiNNaker2 Chip: A Many-Core Platform for Flexible and Scalable Brain-Inspired Computing.”
Abstract Excerpt: The paper presents SpiNNaker2 as a chip that “bridges the gap between deep networks and neuromorphic computing” and reports “ up to 4.5 TOPS in high performance mode and up to 2.7 TOPS/W efficiency in high efficiency mode for INT8 workloads.”
Find the technical paper here.
See comments from Christian Mayer here.
S. Scholze et al., “The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing,” in IEEE Open Journal of Circuits and Systems, doi: 10.1109/OJCAS.2026.3714974.