An Efficient MPI Implementation for Multi-Core Neuromorphic Platforms

Multicore neuromorphic platforms come with a custom library for efficient development of neural network simulations. While these architectures are mainly focused on realtime biological network simulation using detailed neuron models, their application to a wider range of computational tasks is increasing. The reason is their effective support for parallel computation characterised by an intensive communication among processing nodes and their inherent energy efficiency. However, to unlock the full potential of these architectures for a wide range of applications, a library support for a more general computational model has to be developed. This work focuses on the implementation of a standard MPI interface for parallel programming of neuromorphic multicore architectures. The MPI library has been developed on top of the SpiNNaker multi-core neuromorphic platform, featuring a toroid interconnect and packet support for multicast communication. The proposed MPI implementation has been evaluated using an N-body simulation kernel, showing very good efficiency and suggesting that the considered neuromorphic platform with our MPI library is very promising for communication-intensive applications.