Reproducibility Report for the Paper: “Evaluating Performance of Spintronics-Based Spiking Neural Network Chips using Parallel Discrete Event Simulation”

The examined paper introduces Doryta, a Parallel Discrete Event Simulation model for ROSS that runs Spiking Neural Networks. The authors have uploaded their artifact to Zenodo, which ensures a long-term retention of the artifact. This paper can thus receive the Artifacts Available badge. The artifact allows for easy re-running of experiments for the one image and part of the table data in csv format, and one-liner command-lines are provided to run the remaining experiments, the data for which has to be extracted by hand. The dependencies are well documented. The software in the artifact runs correctly with minimal intervention, and is relevant to the paper, earning the Artifacts Evaluated—Functional badge. Furthermore, since the artifact uses well-known, state-of-the-art Python libraries to train Neural Network models and Doryta can read them directly, the paper is assigned the Artifacts Evaluated—Reusable badge. Due to technical limitations, distributed strong scaling experiments could not be reproduced.