Parallel Brain Simulator: A Multi-scale and Parallel Brain-Inspired Neural Network Modeling and Simulation Platform

Abstract

The brain is naturally a parallel and distributed system. Reverse engineering a cognitive brain is considered to be a grand challenge. In this paper, we present the parallel brain simulator (PBS), a parallel and distributed platform for modeling the cognitive brain at multiple scales. Inspired by large-scale graph computation, PBS can be considered as a universal parallel execution engine, which is aimed at reducing the complexity of distributed programming and providing an easy to use programmable platform for computational neuroscientists and artificial intelligence researchers for modeling and simulation of large-scale neural networks. As illustrative examples and validations, three brain-inspired neural networks which are built on PBS are introduced, including the 1:1 human hippocampus network, the 1:1 mouse whole-brain network and the CASIA brain simulator built for cognitive robotics. We deploy PBS on both commodity clusters and supercomputers, and a scalable performance is achieved. In addition, we provide evaluations on the scalability and performance of both lumped synapse-based simulation and non-lumped synapse-based simulation with different data-graph distribution methods to show the effectiveness and usability of the PBS platform.