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Adaptive extreme edge computing for wearable devices
Wearable devices are a fast-growing technology with impact on personal healthcare for both
society and economy. Due to the widespread of sensors in pervasive and distributed …
society and economy. Due to the widespread of sensors in pervasive and distributed …
Neuromorphic computing at scale
Neuromorphic computing is a brain-inspired approach to hardware and algorithm design
that efficiently realizes artificial neural networks. Neuromorphic designers apply the …
that efficiently realizes artificial neural networks. Neuromorphic designers apply the …
A self-adaptive hardware with resistive switching synapses for experience-based neurocomputing
Neurobiological systems continually interact with the surrounding environment to refine their
behaviour toward the best possible reward. Achieving such learning by experience is one of …
behaviour toward the best possible reward. Achieving such learning by experience is one of …
The SpiNNaker 2 processing element architecture for hybrid digital neuromorphic computing
This paper introduces the processing element architecture of the second generation
SpiNNaker chip, implemented in 22nm FDSOI. On circuit level, the chip features adaptive …
SpiNNaker chip, implemented in 22nm FDSOI. On circuit level, the chip features adaptive …
Dendritic computing: branching deeper into machine learning
In this paper, we discuss the nonlinear computational power provided by dendrites in
biological and artificial neurons. We start by briefly presenting biological evidence about the …
biological and artificial neurons. We start by briefly presenting biological evidence about the …
[كتاب][B] Spinnaker-a spiking neural network architecture
20 years in conception and 15 in construction, the SpiNNaker project has delivered the
world's largest neuromorphic computing platform incorporating over a million ARM mobile …
world's largest neuromorphic computing platform incorporating over a million ARM mobile …
Comparing Loihi with a SpiNNaker 2 prototype on low-latency keyword spotting and adaptive robotic control
We implemented two neural network based benchmark tasks on a prototype chip of the
second-generation SpiNNaker (SpiNNaker 2) neuromorphic system: keyword spotting and …
second-generation SpiNNaker (SpiNNaker 2) neuromorphic system: keyword spotting and …
E-prop on SpiNNaker 2: Exploring online learning in spiking RNNs on neuromorphic hardware
Introduction In recent years, the application of deep learning models at the edge has gained
attention. Typically, artificial neural networks (ANNs) are trained on graphics processing …
attention. Typically, artificial neural networks (ANNs) are trained on graphics processing …
SpiNNaker2: A large-scale neuromorphic system for event-based and asynchronous machine learning
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 …
accelerators such as GPUs and TPUs took over many domains of machine learning …
Plasticity and adaptation in neuromorphic biohybrid systems
Neuromorphic systems take inspiration from the principles of biological information
processing to form hardware platforms that enable the large-scale implementation of neural …
processing to form hardware platforms that enable the large-scale implementation of neural …