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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. (PI), Arvin, F. (CoI), Brown, G. (CoI), Carrasco Gomez, J. (CoI), Da Via, C. (CoI), Furber, S. (CoI), Luján, M. (CoI), Watson, S. (CoI), Watts, S. (CoI) & Weightman, A. (CoI)
2/10/17 → 31/03/22
Project: Research