18 citations · 18 across the 4 of their papers we have counts for
4 papers
The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing
Stefan Scholze, Johannes Partzsch, Sebastian Höppner +27
In deep learning, efficiency gets more and more important to compensate for the ongoing growth in model sizes and applications. Neuromorphic hardware has long been advocated as an…
TinyML Towards Industry 4.0: Resource-Efficient Process Monitoring of a Milling Machine
Tim Langer, Matthias Widra, Volkhard Beyer
In the context of industry 4.0, long-serving industrial machines can be retrofitted with process monitoring capabilities for future use in a smart factory. One possible approach is…
An End-to-End DNN Inference Framework for the SpiNNaker2 Neuromorphic MPSoC
Matthias Jobst, Tim Langer, Chen Liu +3
This work presents a multi-layer DNN scheduling framework as an extension of OctopuScheduler, providing an end-to-end flow from PyTorch models to inference on a single SpiNNaker2 c…
SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning
Hector A. Gonzalez, Jiaxin Huang, Florian Kelber +11
The joint progress of artificial neural networks (ANNs) and domain specific hardware accelerators such as GPUs and TPUs took over many domains of machine learning research. This de…