Unraveling the mysteries of brain function requires studying large-scale neural networks and conducting complex simulations. However, achieving hyper-real-time simulations, which would allow in-depth parameter scans and the exploration of slow processes like learning and memory, remains a challenge. Even the fastest supercomputers fall short. But there’s hope! A groundbreaking approach using a hybrid software-hardware architecture, specifically a System-on-Chip (SoC) design, shows promise. By leveraging the powerful capabilities of Xilinx Zynq-7000 SoC device with its FPGA and dual-core ARM Cortex-A9 processor, this design enables the creation of smaller neuromorphic computing clusters. These clusters can simulate networks with tens of thousands of neurons in hyper-real-time, meeting the extensive modeling and simulation demands in neuroscience. Through the application of SoC device technology, researchers are gaining new tools to advance the understanding of brain function. If you’re interested in learning more about this exciting development, check out the research article!

Despite the great strides neuroscience has made in recent decades, the underlying principles of brain function remain largely unknown. Advancing the field strongly depends on the ability to study large-scale neural networks and perform complex simulations. In this context, simulations in hyper-real-time are of high interest, as they would enable both comprehensive parameter scans and the study of slow processes, such as learning and long-term memory. Not even the fastest supercomputer available today is able to meet the challenge of accurate and reproducible simulation with hyper-real acceleration. The development of novel neuromorphic computer architectures holds out promise, but the high costs and long development cycles for application-specific hardware solutions makes it difficult to keep pace with the rapid developments in neuroscience. However, advances in System-on-Chip (SoC) device technology and tools are now providing interesting new design possibilities for application-specific implementations. Here, we present a novel hybrid software-hardware architecture approach for a neuromorphic compute node intended to work in a multi-node cluster configuration. The node design builds on the Xilinx Zynq-7000 SoC device architecture that combines a powerful programmable logic gate array (FPGA) and a dual-core ARM Cortex-A9 processor extension on a single chip. Our proposed architecture makes use of both and takes advantage of their tight coupling. We show that available SoC device technology can be used to build smaller neuromorphic computing clusters that enable hyper-real-time simulation of networks consisting of tens of thousands of neurons, and are thus capable of meeting the high demands for modeling and simulation in neuroscience.

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