SpiNNaker: A 1-W 18-Core System-on-Chip for Massively-Parallel Neural Network Simulation
The modelling of large systems of spiking neurons is computationally very demanding in terms of processing power and communication. SpiNNaker - Spiking Neural Network architecture - is a massively parallel computer system designed to provide a cost-effective and flexible simulator for neuroscience e...
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| Vydáno v: | IEEE journal of solid-state circuits Ročník 48; číslo 8; s. 1943 - 1953 |
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| Hlavní autoři: | , , , , , , , , |
| Médium: | Journal Article Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
New York, NY
IEEE
01.08.2013
Institute of Electrical and Electronics Engineers |
| Témata: | |
| ISSN: | 0018-9200, 1558-173X |
| On-line přístup: | Získat plný text |
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| Shrnutí: | The modelling of large systems of spiking neurons is computationally very demanding in terms of processing power and communication. SpiNNaker - Spiking Neural Network architecture - is a massively parallel computer system designed to provide a cost-effective and flexible simulator for neuroscience experiments. It can model up to a billion neurons and a trillion synapses in biological real time. The basic building block is the SpiNNaker Chip Multiprocessor (CMP), which is a custom-designed globally asynchronous locally synchronous (GALS) system with 18 ARM968 processor nodes residing in synchronous islands, surrounded by a lightweight, packet-switched asynchronous communications infrastructure. In this paper, we review the design requirements for its very demanding target application, the SpiNNaker micro-architecture and its implementation issues. We also evaluate the SpiNNaker CMP, which contains 100 million transistors in a 102-mm 2 die, provides a peak performance of 3.96 GIPS, and has a peak power consumption of 1 W when all processor cores operate at the nominal frequency of 180 MHz. SpiNNaker chips are fully operational and meet their power and performance requirements. |
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| ISSN: | 0018-9200 1558-173X |
| DOI: | 10.1109/JSSC.2013.2259038 |