Verification of a neuromorphic computing network simulator using experimental traffic data - Kleijnen, Robert; Robens, Markus; Schiek, Michael; van Waasen, Stefan (Frontiers in Neuroscience, 2022)
Simulations are a powerful tool to explore the design space of hardware systems, offering the flexibility to analyze different designs by simply changing parameters within the simulator setup. A precondition for the effectiveness of this methodology is that the simulation results accurately represent the later real system. In a previous work, we introduced a simulator specifically designed to estimate the network load and latency to be observed on the connections in neuromorphic computing systems. The simulator was shown to be especially valuable in case of large scale heterogeneous neural networks. In this work, we compare the network load measured on a SpiNNaker board running a neural network in different configurations reported in literature to the results obtained with our simulator running the same configurations. The simulated network loads show minor differences to the values reported in the ascribed publication, but fall within the margin of error, considering the generation of the test case neural network based on statistics which introduces variations. Showing that the network simulator provides representative results for this type of ---biological plausible---heterogeneous neural networks, paves the way to further use the simulator for more complex network analyses.