OctopuScheduler: On-Chip DNN Scheduling on the SpiNNaker2 Neuromorphic MPSoC
We present OctopuScheduler, the first generalized on-chip scheduling framework for the accelerated inference of non-spiking deep neural networks (DNNs) on the neuromorphic hardware platform SpiNNaker2. The goal of OctopuScheduler is to flexibly support a wide variety of state-of-the-art DNN architectures for different domains, moving from an application-specific custom implementation to a generally applicable framework, simplifying the access to the SpiNNaker2 platform. The on-chip scheduling approach allows to minimize communication latencies with the host, completely controlling the execution of layers for convolutional neural networks (CNNs) and transformer architectures within a single chip.OctopuScheduler as a scheduling framework for classical deep neural networks has the potential to unlock experimentation with large-scale hybrid deep and spiking neural network (SNN) architectures, event-based computing and neuromorphic modifications of classical state-of-the-art DNN architectures on the neuromorphic multi-processor system-on-chip (MPSoC) SpiNNaker2.
Authors
- Florian Kelber (ORCID: https://orcid.org/0000-0001-7663-5211)
- Matthias Jobst (ORCID: https://orcid.org/0000-0003-3147-1625)
- Tim Langer (ORCID: https://orcid.org/0000-0002-9278-058X)
- Christian Mayr (ORCID: https://orcid.org/0000-0003-3502-0872)
- Bernhard Vogginger (ORCID: https://orcid.org/0000-0001-9042-5405)
- Chen Liu (ORCID: https://orcid.org/0000-0002-3288-9516)
Institutions
- Technische Universität Dresden (DE)
Publication Details
- Published
- 2025-03-25
- DOI
- https://doi.org/10.1109/nice65350.2025.11065866
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministry of Education