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Yan, Yexin; Kappel, David; Neumarker, Felix; Partzsch, Johannes; Vogginger, Bernhard; Hoppner, Sebastian; Furber, Steve; Maass, Wolfgang; Legenstein, Robert; Mayr, Christian (2019). "Efficient Reward-Based
Structural Plasticity on a SpiNNaker 2 Prototype".
535:
A description of the
Globally Asynchronous, Locally Synchronous (GALS) nature of SpiNNaker, with an overview of the asynchronous communications hardware designed to transmit neural 'spikes' between processors.
587:
Modelling and analysis of the SpiNNaker interconnect in a million-core machine, showing the suitability of the packet-switched network for large-scale spiking neural network simulation.
1917:
495:
A manifesto for the SpiNNaker project, surveying and reviewing the general level of understanding of brain function and approaches to building computer modelof the brain.
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199:
On 24 September 2019 HBP announced that an 8 million euro grant, that will fund construction of the second generation machine, (called SpiNNcloud) has been given to
140:
178:. In total, the goal is to simulate the behaviour of aggregates of up to a billion neurons in real time. This machine requires about 100 kW from a 240 V
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A demonstration of SpiNNaker's ability to simulate different neural models (simultaneously, if necessary) in contrast to other neuromorphic hardware.
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Four-chip, real-time simulation of a four-million-synapse cortical circuit, showing the extreme energy efficiency of the SpiNNaker architecture
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Navaridas, J.; Luján, M.; Miguel-Alonso, J.; Plana, L. A.; Furber, S. (2009). "Understanding the interconnection network of SpiNNaker".
1023:
1902:
906:
405:
1696:
509:; Temple, S.; Khan, M.; Shi, Y.; Wu, J.; Yang, S. (2007). "A GALS Infrastructure for a Massively Parallel Multiprocessor".
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2008 IEEE International Joint
Conference on Neural Networks (IEEE World Congress on Computational Intelligence)
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1123:
1080:
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1028:
388:; Woods, J. V. (2008). "Efficient modelling of spiking neural networks on a scalable chip multiprocessor".
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601:(2011). "Concurrent heterogeneous neural model simulation on real-time neuromimetic hardware".
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Proceedings of the 23rd international conference on
Conference on Supercomputing - ICS '09
8:
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872:"Second Generation SpiNNaker Neuromorphic Supercomputer to be Built at TU Dresden - News"
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170:, with each rack holding over 100,000 cores. The cards holding the chips are held in 5
641:(2012). "Power-efficient simulation of detailed cortical microcircuits on SpiNNaker".
151:, totalling 1,036,800 cores and over 7 TB of RAM. The computing platform is based on
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Rast, A.; Galluppi, F.; Davies, S.; Plana, L.; Patterson, C.; Sharp, T.; Lester, D.;
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790:
Monroe, D. (2014). "Neuromorphic computing gets ready for the (really) big time".
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978:
828:"SpiNNaker brain simulation project hits one million cores on a single machine"
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28:
The SpiNNaker 1 million core machine assembled at the
University of Manchester
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designed by the
Advanced Processor Technologies Research Group (APT) at the
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announced that the million core milestone had been achieved.
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144:
143:. It is composed of 57,600 processing nodes, each with 18
218:
347:
1918:
Department of
Computer Science, University of Manchester
141:
Department of
Computer Science, University of Manchester
636:
596:
504:
751:"The Human Brain Project and neuromorphic computing"
748:
222:
IEEE Transactions on
Biomedical Circuits and Systems
688:Video interview by computerphile with Steve Furber
383:
351:; Galluppi, F.; Temple, S.; Plana, L. A. (2014).
1879:
683:
681:
185:SpiNNaker is being used as one component of the
851:SpiNNaker: 1 million core neuromorphic platform
289:Advanced Processor Technologies Research Group
17:SpiNNaker: spiking neural network architecture
1862:
907:
847:
678:
1923:Science and technology in Greater Manchester
921:
700:"SpiNNaker Project - Architectural Overview"
450:
212:
429:A million ARM cores to host brain simulator
128:(spiking neural network architecture) is a
1869:
1855:
914:
900:
749:Calimera, A; Macii, E; Poncino, M (2013).
22:
766:
725:"SpiNNaker Project - Boards and Machines"
554:
480:
368:
233:
301:"SpiNNaker Project - The SpiNNaker Chip"
1880:
789:
461:Journal of the Royal Society Interface
189:platform for the Human Brain Project.
895:
166:The completed design is housed in 10
1821:
1752:Generative adversarial network (GAN)
341:
182:and an air-conditioned environment.
147:(specifically ARM968) and 128 MB of
637:Sharp, T.; Galluppi, F.; Rast, A.;
511:IEEE Design & Test of Computers
438:News article on the project in the
377:
13:
318:
14:
1939:
848:Petrut Bogdan (14 October 2018),
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643:Journal of Neuroscience Methods
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174:, and each core emulates 1,000
1702:Recurrent neural network (RNN)
1692:Differentiable neural computer
655:10.1016/j.jneumeth.2012.03.001
498:
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422:
293:
282:
1:
1903:Computational fields of study
1747:Variational autoencoder (VAE)
1707:Long short-term memory (LSTM)
974:Computational learning theory
206:
1841:. You can help Knowledge by
1727:Convolutional neural network
615:10.1016/j.neunet.2011.06.014
457:"Neural systems engineering"
7:
1722:Multilayer perceptron (MLP)
155:, useful in simulating the
10:
1944:
1898:Computational neuroscience
1820:
1798:Artificial neural networks
1712:Gated recurrent unit (GRU)
938:Differentiable programming
398:10.1109/IJCNN.2008.4634194
370:10.1109/JPROC.2014.2304638
330:, University of Manchester
252:10.1109/TBCAS.2019.2906401
137:supercomputer architecture
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1131:Artificial neural network
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954:Automatic differentiation
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793:Communications of the ACM
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89:
81:
71:
63:
53:
43:
33:
21:
959:Neuromorphic engineering
922:Differentiable computing
876:www.humanbrainproject.eu
1928:Computer hardware stubs
1732:Residual neural network
1148:Artificial Intelligence
729:apt.cs.manchester.ac.uk
704:apt.cs.manchester.ac.uk
565:10.1145/1542275.1542317
357:Proceedings of the IEEE
353:"The SpiNNaker Project"
305:apt.cs.manchester.ac.uk
192:On 14 October 2018 the
153:spiking neural networks
473:10.1098/rsif.2006.0177
392:. pp. 2812–2819.
187:neuromorphic computing
1913:Computer architecture
1687:Neural Turing machine
1275:Human image synthesis
1778:Computer programming
1757:Graph neural network
1332:Text-to-video models
1310:Text-to-image models
1158:Large language model
1143:Scientific computing
949:Statistical manifold
944:Information geometry
755:Functional Neurology
523:10.1109/MDT.2007.149
434:17 July 2011 at the
77:ARM968E-S @ 200 MHz
48:Manchester computers
1124:In-context learning
964:Pattern recognition
327:SpiNNaker Home Page
244:2019arXiv190308500Y
161:Human Brain Project
18:
1717:Echo state network
1605:JĂĽrgen Schmidhuber
1300:Facial recognition
1295:Speech recognition
1205:Software libraries
130:massively parallel
16:
1850:
1849:
1835:computer hardware
1813:
1812:
1575:Stephen Grossberg
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1547:
407:978-1-4244-1820-6
123:
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1935:
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1803:Machine learning
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1528:Action selection
1518:Self-driving car
1325:Stable Diffusion
1290:Speech synthesis
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1119:Machine learning
995:Gradient descent
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149:mobile DDR SDRAM
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1908:AI accelerators
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1641:Google DeepMind
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1585:Geoffrey Hinton
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1407:Project Debater
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1251:Implementations
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1049:Backpropagation
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969:Tensor calculus
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603:Neural Networks
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556:10.1.1.634.9481
549:. p. 286.
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467:(13): 193–206.
449:
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436:Wayback Machine
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145:ARM9 processors
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1893:Supercomputers
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1615:Ilya Sutskever
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1580:Demis Hassabis
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1570:Ian Goodfellow
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1153:Language model
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1103:Autoregression
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1081:Regularization
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1044:Loss functions
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979:Inductive bias
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649:(1): 110–118.
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609:(9): 961–978.
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505:Plana, L. A.;
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363:(5): 652–665.
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1560:Yoshua Bengio
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1540:Robot control
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1457:Chinchilla AI
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1136:Deep learning
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1417:IBM Watsonx
1039:Convolution
1017:Overfitting
734:17 November
709:17 November
310:17 November
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107:.manchester
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1882:Categories
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1636:EleutherAI
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1503:Q-learning
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1600:Andrew Ng
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134:manycore
1656:Meta AI
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240:Bibcode
176:neurons
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1442:GPT-4
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