Using Neuromorphic Hardware for the Scalable Execution of Massively Parallel, Communication-Intensive Algorithms

Neuromorphic hardware like SpiNNaker offers massive parallelism and efficient communication of small payloads to accelerate the simulation of spiking neurons in neural networks. In this paper, we demonstrate that this hardware is also beneficial for other for applications which require massive parallelism and the large-scale exchange of small messages. More specifically, we study the scalability of PageRank on SpiNNaker and compare it to an implementation on traditional hardware. In our experiments, we show that PageRank on SpiNNaker scales better than on traditional multicore architectures.