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Abstract
SpiNNaker is a massively-parallel computer system optimised for the simulation, in real time, of very large networks of spiking neurons. The system consists of over 1 million, energy-efficient ARM cores distributed over 57,600 SpiNNaker chips, each of which contains 18 cores interconnected by a neurobiologically-inspired, asynchronous (clock-less) Network-on-Chip. The NoC is extended to the chip boundary for chip-to-chip communication. To construct the massively-parallel system, SpiNNaker boards, housing 48 SpiNNaker chips, are connected together using FPGA-based, high-speed serial links. This paper presents some of the novel aspects of the design and implementation of the bespoke interconnect, including a credit-based, reliable frame transport protocol that allows the multiplexing of asynchronous SpiNNaker channels over the serial links, and an efficient FPGA-to-SpiNNaker chip interface that provides
twice the throughput of traditional asynchronous interfaces. SpiNNaker houses 3,600 Xilinx Spartan-6 FPGAs, provides a bisection bandwidth of 480 Gbit/s, and ran the first-ever, true real-time brain cortical simulation [1] – a feat not currently achievable using conventional HPCs or GPUs.
twice the throughput of traditional asynchronous interfaces. SpiNNaker houses 3,600 Xilinx Spartan-6 FPGAs, provides a bisection bandwidth of 480 Gbit/s, and ran the first-ever, true real-time brain cortical simulation [1] – a feat not currently achievable using conventional HPCs or GPUs.
Original language | English |
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Article number | 9079810 |
Pages (from-to) | 84918-84928 |
Number of pages | 11 |
Journal | IEEE Access |
Volume | 8 |
DOIs | |
Publication status | Published - 28 Apr 2020 |
Keywords
- High-speed interconnect
- asynchronous interface
- field-programmable gate array (FPGA)
- neuromorphic or neurobiologically-inspired computing
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Dive into the research topics of 'spiNNlink: FPGA-Based Interconnect for the Million-Core SpiNNaker System'. Together they form a unique fingerprint.Projects
- 1 Finished
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Robotics and Artificial Intelligence for Nuclear (RAIN)
Lennox, B., Arvin, F., Brown, G., Carrasco Gomez, J., Da Via, C., Furber, S., Luján, M., Watson, S., Watts, S. & Weightman, A.
2/10/17 → 31/03/22
Project: Research