Kauth Kevin, Stadtmann Tim, Sobhani Vida, Gemmeke Tobias
Chair of Integrated Digital Systems and Circuit Design, RWTH Aachen University, Aachen, Germany.
Front Comput Neurosci. 2023 Apr 20;17:1144143. doi: 10.3389/fncom.2023.1144143. eCollection 2023.
Research in the field of computational neuroscience relies on highly capable simulation platforms. With real-time capabilities surpassed for established models like the cortical microcircuit, it is time to conceive next-generation systems: neuroscience simulators providing significant acceleration, even for larger networks with natural density, biologically plausible multi-compartment models and the modeling of long-term and structural plasticity.
Stressing the need for agility to adapt to new concepts or findings in the domain of neuroscience, we have developed the neuroAIx-Framework consisting of an empirical modeling tool, a virtual prototype, and a cluster of FPGA boards. This framework is designed to support and accelerate the continuous development of such platforms driven by new insights in neuroscience.
Based on design space explorations using this framework, we devised and realized an FPGA cluster consisting of 35 NetFPGA SUME boards.
This system functions as an evaluation platform for our framework. At the same time, it resulted in a fully deterministic neuroscience simulation system surpassing the state of the art in both performance and energy efficiency. It is capable of simulating the microcircuit with 20× acceleration compared to biological real-time and achieves an energy efficiency of 48nJ per synaptic event.
计算神经科学领域的研究依赖于功能强大的模拟平台。对于像皮质微电路这样的既定模型,其实时能力已被超越,现在是构思下一代系统的时候了:神经科学模拟器要能提供显著加速,即使对于具有自然密度的更大网络、具有生物学合理性的多室模型以及长期和结构可塑性的建模。
强调适应神经科学领域新概念或新发现的灵活性的必要性,我们开发了由经验建模工具、虚拟原型和一组FPGA板组成的neuroAIx框架。该框架旨在支持和加速由神经科学新见解驱动的此类平台的持续开发。
基于使用该框架的设计空间探索,我们设计并实现了一个由35个NetFPGA SUME板组成的FPGA集群。
该系统作为我们框架的评估平台。同时,它产生了一个完全确定性的神经科学模拟系统,在性能和能源效率方面都超越了现有技术水平。与生物实时相比,它能够以20倍的加速模拟微电路,并且每个突触事件的能源效率达到48纳焦。