Adaptive extreme edge computing for wearable devices

E Covi, E Donati, X Liang, D Kappel… - Frontiers in …, 2021‏ - frontiersin.org
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 …

Neuromorphic computing at scale

D Kudithipudi, C Schuman, CM Vineyard, T Pandit… - Nature, 2025‏ - nature.com
Neuromorphic computing is a brain-inspired approach to hardware and algorithm design
that efficiently realizes artificial neural networks. Neuromorphic designers apply the …

A self-adaptive hardware with resistive switching synapses for experience-based neurocomputing

S Bianchi, I Muñoz-Martin, E Covi, A Bricalli… - Nature …, 2023‏ - nature.com
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 …

The SpiNNaker 2 processing element architecture for hybrid digital neuromorphic computing

S Höppner, Y Yan, A Dixius, S Scholze… - arxiv preprint arxiv …, 2021‏ - arxiv.org
This paper introduces the processing element architecture of the second generation
SpiNNaker chip, implemented in 22nm FDSOI. On circuit level, the chip features adaptive …

Dendritic computing: branching deeper into machine learning

J Acharya, A Basu, R Legenstein, T Limbacher… - Neuroscience, 2022‏ - Elsevier
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 …

[كتاب][B] Spinnaker-a spiking neural network architecture

S Furber, P Bogdan - 2020‏ - library.oapen.org
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 …

Comparing Loihi with a SpiNNaker 2 prototype on low-latency keyword spotting and adaptive robotic control

Y Yan, TC Stewart, X Choo, B Vogginger… - Neuromorphic …, 2021‏ - iopscience.iop.org
We implemented two neural network based benchmark tasks on a prototype chip of the
second-generation SpiNNaker (SpiNNaker 2) neuromorphic system: keyword spotting and …

E-prop on SpiNNaker 2: Exploring online learning in spiking RNNs on neuromorphic hardware

A Rostami, B Vogginger, Y Yan, CG Mayr - Frontiers in Neuroscience, 2022‏ - frontiersin.org
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 …

SpiNNaker2: A large-scale neuromorphic system for event-based and asynchronous machine learning

HA Gonzalez, J Huang, F Kelber, KK Nazeer… - arxiv preprint arxiv …, 2024‏ - arxiv.org
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 …

Plasticity and adaptation in neuromorphic biohybrid systems

R George, M Chiappalone, M Giugliano, T Levi… - Iscience, 2020‏ - cell.com
Neuromorphic systems take inspiration from the principles of biological information
processing to form hardware platforms that enable the large-scale implementation of neural …