Optimized task graph mapping on a many-core neuromorphic supercomputer

This paper presents an approach for improving the overall performance of a general purpose application running as a task graph on a many-core neuromorphic supercomputer. Our task graph framework is based on graceful degradation and amelioration paradigms that strive to achieve high reliability and performance by incorporating fault tolerance and task spawning features. The optimization is applied on an instance of the task graph by performing a soft load balancing on the data traffic between nodes in the graph. We implemented the framework and its optimization on SpiNNaker, a many-core neuromorphic platform containing a million ARM9 processing cores. We evaluate our method using several static mapping examples, where some of them were generated using an evolutionary algorithm. The experiment demonstrates that a performance improvement of up to 8.2% can be achieved when implementing our algorithm on a fully-utilized SpiNNaker communication infrastructure.