Speculative Distributed Simulation of Very Large Spiking Neural Networks

Adriano Pimpini, Andrea Piccione, Bruno Ciciani, and Alessandro Pellegrini

Published in: Proceedings of the 2022 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation
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Spiking Neural Networks are a class of Artificial Neural Networks that closely mimic biological neural networks. They are particularly interesting because of their potential to advance research in several fields, both because of better insights on neural behaviour (benefiting medicine, neuroscience, psychology) and the potential in Artificial Intelligence. Their ability to run on a low energy budget once implemented in hardware makes them even more appealing. However, because of their behaviour that evolves with time, when a hardware implementation is not available, their output cannot simply be computed with a one-shot function (however complex), but instead they need to be simulated.
Simulating Spiking Neural Networks is exceptionally costly, mainly due to their sheer size. Many current simulation methods have trouble scaling up on more powerful systems because of conservative synchronisation methods. Scalability is often offered through approximation of the actual results. In this paper, we present a modelling methodology and runtime-environment support adhering to the Time Warp synchronisation protocol, which enables speculative distributed simulation of Spiking Neural Network models with improved accuracy of the results. We discuss the methodological and technical aspects that will allow effective speculative simulation and present an experimental assessment on large virtualised environments, which shows the viability of simulating networks made of millions of neurons.

BibTeX Entry:

author = {Pimpini, Adriano and Piccione, Andrea and Ciciani, Bruno and Pellegrini, Alessandro},
booktitle = {Proceedings of the 2022 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation},
title = {Speculative Distributed Simulation of Very Large Spiking Neural Networks},
year = {2022},
month = jun,
publisher = {ACM},
series = {PADS},
doi = {10.1145/3518997.3531027},
location = {Atlanta, GA, USA},
badges = {available,reusable}