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Summary

Two new simulation frameworks integrate network-on-chip simulators with the NEST environment for brain modeling. These frameworks enable faster-than-real-time simulations and provide insights into neural network communication latency.

Keywords:

co-simulation latency multicast network-on-chip simulator neuromorphic computing spiking neural networks

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Area of Science:

Computational neuroscience
Computer engineering

Background:

Understanding the brain's computational mechanisms requires simulating large-scale neural networks.
Simulating these complex models presents challenges for existing platforms, particularly regarding inter-node communication latency.
Faster-than-real-time simulations are crucial for studying slow neural processes like learning.

Purpose of the Study:

To present two novel simulation frameworks that interface network-on-chip (NoC) simulators with the NEST (Neural Simulation Tool) environment.
To enable direct definition of network traffic by neural network models for steering NoC simulations.
To obtain statistics on network latencies and explore accelerated simulation possibilities.

Main Methods:

Developed two simulation frameworks integrating NoC simulators with the NEST environment.
Utilized neural network models to define and steer NoC simulations, generating network traffic.
Applied frameworks to scaled versions of the cortical microcircuit model to analyze performance, latency, and traffic.
Implemented a time stamp conversion mechanism to emulate acceleration factors.

Main Results:

The frameworks successfully generated network traffic based on neural network models and provided latency statistics.
Performance curves, latency, and traffic distributions were determined for scaled cortical microcircuit models.
The second framework, featuring tree-based multicast support and torus topology, yielded optimal results.
Results align with previous findings on node internals, suggesting accelerated simulations are achievable.

Conclusions:

The developed simulation frameworks offer a viable approach for studying large-scale neural networks and their communication dynamics.
The integration with NEST and NoC simulators facilitates efficient, accelerated simulations of complex brain models.
Further refinement, considering NoC simulator assumptions, can enhance the accuracy and applicability of these simulation techniques.