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\title{Page Rank on neuromorphic \\SpiNNaker hardware}
\author{Louis Blin (CID: 00963306)}

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The rise of artificial intelligence applications during the past years has led to an increasing demand for new specialised hardware. Consequently, a European-wide research initiative has built the Spiking Neural Network Architecture (SpiNNaker) machine, a neuromorphic computer with a biology-inspired hardware design. This novel design has allowed for faithful neural simulations, but also has revealed new performance opportunities for other problem classes, such as graph processing with Page Rank as a benchmark. Indeed, a SpiNNaker machine can hold up to a million cores and is wired with very low latency inter-core links, which are optimised for transfers of very large numbers of small-sized data packets. In this project, we take these hardware specificities as opportunities by porting Page Rank to SpiNNaker to investigate its scaling potential on the machine compared to a regular personal computer implementation. The contributions of this project are twofold: (a) we built a generic user-friendly framework that supports Page Rank simulations on SpiNNaker (b) we demonstrated that Page Rank under SpiNNaker brings a speed-up of over a factor of 3.

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I would like to sincerely thank my supervisor Dr. Thomas Heinis for his continuous guidance throughout this project and allowing me to gain this exposure to the world of neuromorphic computing. Thank you also to Dr. Ahsan J. Awan and to Dr. Jana Giveca for their insightful pointers and thorough feedback concerning the writing this report. Finally, I am also grateful for the help I received from the academic staff of the Advanced Processor Technologies (APT) Research Group at the University of Manchester, who built the machine at the core of this project.

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