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.

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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

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article

OctopuScheduler: On-Chip DNN Scheduling on the SpiNNaker2 Neuromorphic MPSoC

Florian Kelber, Matthias Jobst, Tim Langer, Christian Mayr et al.
Advanced Memory and Neural Computing
article

OctopuScheduler: On-Chip DNN Scheduling on the SpiNNaker2 Neuromorphic MPSoC

Florian Kelber, Matthias Jobst, Tim Langer, Christian Mayr, Bernhard Vogginger, Chen Liu
article en

Abstract

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.

Technische Universität Dresden (DE)
Ministry of Education
Openalex Percentile: Top 85%
Advanced Memory and Neural Computing
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