A Digital Twin Network for Computational Neuroscience Simulators: Exploring Network Architectures for Acceleration of Biological Neural Network Simulations

Abstract

Despite recent advances in the domain of Computational Neuroscience (CN), the accelerated simulation of large-scale biological networks of natural density is daunting due to scalability issues and communication constraints of existing CN simulators. This challenge can be addressed by developing an ultra-low latency packet delivery service among the tightly coupled processing nodes of a CN simulator. Given that CN is a rapidly evolving field, constant updates are inevitable during the life of a CN simulator. Hence, a digital twin network for CN simulators is crucial as it can enable low-cost prototyping to keep their network up to date with ever-changing requirements caused by advances in CN. To this end, we have developed a framework to replicate the dynamic network behavior of potential CN simulators. The core of our framework is based on high-end FPGA boards with high data rate transceivers, enabling emulation of various network architectures with different topologies and executed protocols. The precise dynamic behavior of the FPGA-based digital twin provides accurate modeling and reliable assessments of real-time dynamics. To expedite the exploration and prototyping process, we use the FPGA cluster in conjunction with in-house software-based network simulators. Our initial evaluations based on the developed software network simulators resulted in an overestimation of network performance. However, after calibration, our results demonstrate the potential of our approach in addressing complex problems such as assessing large-scale networks.