DAMNED: A Distributed and Multithreaded Neural Event-Driven simulation framework

TL;DR

DAMNED is a scalable simulation framework for large-scale Spiking Neural Networks that combines event-driven processing with distributed and multithreaded computing to efficiently simulate neural activity.

Contribution

It introduces a novel framework that integrates distributed topology mapping and multithreaded event processing for efficient large-scale SNN simulation.

Findings

Achieves efficient simulation of large-scale SNNs

Combines event-driven and parallel computing techniques

Provides a distributed scheduling solution without synchronization barriers

Abstract

In a Spiking Neural Networks (SNN), spike emissions are sparsely and irregularly distributed both in time and in the network architecture. Since a current feature of SNNs is a low average activity, efficient implementations of SNNs are usually based on an Event-Driven Simulation (EDS). On the other hand, simulations of large scale neural networks can take advantage of distributing the neurons on a set of processors (either workstation cluster or parallel computer). This article presents DAMNED, a large scale SNN simulation framework able to gather the benefits of EDS and parallel computing. Two levels of parallelism are combined: Distributed mapping of the neural topology, at the network level, and local multithreaded allocation of resources for simultaneous processing of events, at the neuron level. Based on the causality of events, a distributed solution is proposed for solving the complex problem of scheduling without synchronization barrier.