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Towards accessible Parallel Discrete Event Simulation of Spiking Neural Networks

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Adriano PimpiniAuthors Info & Claims

SIGSIM-PADS '23: Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

Pages 60 - 61

https://doi.org/10.1145/3573900.3593637

Published: 21 June 2023 Publication History

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Abstract

Spiking Neural Networks (SNNs) are a class of Artificial Neural Networks that closely mimic biological neural networks. Their potential to advance medical and artificial intelligence research makes them particularly interesting to study. Since their behaviour cannot be computed with single one-shot functions, simulations are employed to study their evolution over time.

Recent works presented the possibility of simulating SNNs using speculative Parallel Discrete Event Simulation (PDES). However, no high-level interface to run SNN simulations using PDES was provided, leaving the model implementation to the users. This demanding process creates a barrier to the adoption of the method.

In this work, the initial efforts towards making PDES-based simulation of SNNs easily accessible via interfaces with a high abstraction level (PyNN) are reported. Preliminary performance results are reported and comparisons are made between PDES using the ROme OpTimistic Simulator (ROOT-Sim), and the state-of-the-art SNN simulator NEST, both used through the PyNN interfaces.

References

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Adriano Pimpini, Andrea Piccione, Bruno Ciciani, and Alessandro Pellegrini. 2022. Speculative distributed simulation of very large Spiking Neural Networks. In Proceedings of the 2022 SIGSIM Conference on Principles of Advanced Discrete Simulation(SIGSIM PADS). ACM, New York, NY, USA, 93–104. https://doi.org/10.1145/3518997.3531027

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Adriano Pimpini, Andrea Piccione, and Alessandro Pellegrini. 2022. On the Accuracy and Performance of Spiking Neural Network Simulations. In 2022 IEEE/ACM 26th International Symposium on Distributed Simulation and Real Time Applications (DS-RT)(DS-RT ’22). IEEE, Piscataway, NJ, USA, 96–103. https://doi.org/10.1109/DS-RT55542.2022.9932062

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Cited By

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Cruz-Camacho EQian SShukla AMcGlohon NRakheja SCarothers C(2024)Performance Evaluation of Spintronic-Based Spiking Neural Networks using Parallel Discrete-Event SimulationACM Transactions on Modeling and Computer Simulation10.1145/364946435:1(1-30)Online publication date: 25-Nov-2024
https://dl.acm.org/doi/10.1145/3649464

Index Terms

Towards accessible Parallel Discrete Event Simulation of Spiking Neural Networks
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation types and techniques
      1. Discrete-event simulation

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SIGSIM-PADS '23: Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

June 2023

173 pages

ISBN:9798400700309

DOI:10.1145/3573900

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 21 June 2023

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SIGSIM-PADS '23: SIGSIM Conference on Principles of Advanced Discrete Simulation

June 21 - 23, 2023

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Cruz-Camacho EQian SShukla AMcGlohon NRakheja SCarothers C(2024)Performance Evaluation of Spintronic-Based Spiking Neural Networks using Parallel Discrete-Event SimulationACM Transactions on Modeling and Computer Simulation10.1145/364946435:1(1-30)Online publication date: 25-Nov-2024
https://dl.acm.org/doi/10.1145/3649464

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Abstract

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A Markovian event-based framework for stochastic spiking neural networks

Event and time driven hybrid simulation of spiking neural networks

Parallel simulation techniques for large-scale discrete-event models

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