In today's world, SpiNNaker is an issue that is becoming increasingly relevant in society. Over time, SpiNNaker has become a fundamental aspect in people's daily lives, influencing their decisions and actions. Since SpiNNaker it has evolved and adapted to new trends and technologies, becoming a topic of common interest for a wide variety of people. In this article, we will thoroughly explore the impact of SpiNNaker on today's society and how it has gained importance over the years.
SpiNNaker: spiking neural network architecture
The SpiNNaker 1 million core machine assembled at the University of Manchester
The completed design is housed in 10 19-inch racks, with each rack holding over 100,000 cores. The cards holding the chips are held in 5 blade enclosures, and each core emulates 1,000 neurons. 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 supply and an air-conditioned environment.
SpiNNaker is being used as one component of the neuromorphic computing platform for the Human Brain Project.
On 14 October 2018 the HBP announced that the million core milestone had been achieved.
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 TU Dresden.
References
^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". IEEE Transactions on Biomedical Circuits and Systems. 13 (3): 579–591. arXiv:1903.08500. Bibcode:2019arXiv190308500Y. doi:10.1109/TBCAS.2019.2906401. ISSN1932-4545. PMID30932847. S2CID84186422.
^Xin Jin; Furber, S. B.; Woods, J. V. (2008). "Efficient modelling of spiking neural networks on a scalable chip multiprocessor". 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). pp. 2812–2819. doi:10.1109/IJCNN.2008.4634194. ISBN978-1-4244-1820-6. S2CID2103654.
^Plana, L. A.; Furber, S. B.; Temple, S.; Khan, M.; Shi, Y.; Wu, J.; Yang, S. (2007). "A GALS Infrastructure for a Massively Parallel Multiprocessor". IEEE Design & Test of Computers. 24 (5): 454. doi:10.1109/MDT.2007.149. S2CID16758888. 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.
^Navaridas, J.; Luján, M.; Miguel-Alonso, J.; Plana, L. A.; Furber, S. (2009). "Understanding the interconnection network of SpiNNaker". Proceedings of the 23rd international conference on Conference on Supercomputing - ICS '09. p. 286. CiteSeerX10.1.1.634.9481. doi:10.1145/1542275.1542317. ISBN9781605584980. S2CID3710084. 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.
^Rast, A.; Galluppi, F.; Davies, S.; Plana, L.; Patterson, C.; Sharp, T.; Lester, D.; Furber, S. (2011). "Concurrent heterogeneous neural model simulation on real-time neuromimetic hardware". Neural Networks. 24 (9): 961–978. doi:10.1016/j.neunet.2011.06.014. PMID21778034. A demonstration of SpiNNaker's ability to simulate different neural models (simultaneously, if necessary) in contrast to other neuromorphic hardware.
^Sharp, T.; Galluppi, F.; Rast, A.; Furber, S. (2012). "Power-efficient simulation of detailed cortical microcircuits on SpiNNaker". Journal of Neuroscience Methods. 210 (1): 110–118. doi:10.1016/j.jneumeth.2012.03.001. PMID22465805. S2CID19083072. Four-chip, real-time simulation of a four-million-synapse cortical circuit, showing the extreme energy efficiency of the SpiNNaker architecture