In this work, we present HiAER-Spike, a modular, reconfigurable, event-driven neuromorphic computing platform designed to execute large spiking neural networks with up to 160 million neurons and 40 billion synapses - roughly twice the neurons of a mouse brain at faster than real time. This system, assembled at the UC San Diego Supercomputer Center, comprises a co-designed hard- and software stack that is optimized for run-time massively parallel processing and hierarchical address-event routing (HiAER) of spikes while promoting memory-efficient network storage and execution. The architecture efficiently handles both sparse connectivity and sparse activity for robust and low-latency event-driven inference for both edge and cloud computing. A Python programming interface to HiAER-Spike, agnostic to hardware-level detail, shields the user from complexity in the configuration and execution of general spiking neural networks with minimal constraints in topology. The system is made easily available over a web portal for use by the wider community. In the following, we provide an overview of the hard- and software stack, explain the underlying design principles, demonstrate some of the system's capabilities and solicit feedback from the broader neuromorphic community. Examples are shown demonstrating HiAER-Spike's capabilities for event-driven vision on benchmark CIFAR-10, DVS event-based gesture, MNIST, and Pong tasks.

翻译：本研究提出了HiAER-Spike——一种模块化、可重构的事件驱动神经形态计算平台，其设计用于执行包含多达1.6亿神经元和400亿突触的大型脉冲神经网络，其神经元数量约为小鼠大脑的两倍，且能以快于实时速度运行。该系统在加州大学圣地亚哥分校超级计算中心组装完成，包含一套协同设计的软硬件堆栈，针对运行时大规模并行处理与脉冲的分层地址事件路由（HiAER）进行了优化，同时实现了内存高效的网络存储与执行。该架构能高效处理稀疏连接与稀疏活动，为边缘计算和云计算提供鲁棒且低延迟的事件驱动推理。HiAER-Spike的Python编程接口对硬件细节透明，使用户在配置和执行通用脉冲神经网络时无需面对复杂细节，且对网络拓扑的限制极小。该系统通过门户网站向更广泛的研究社区开放使用。下文将概述其软硬件堆栈，阐释底层设计原理，展示系统的部分功能，并征求神经形态计算社区的反馈。示例展示了HiAER-Spike在基准数据集CIFAR-10、基于DVS的事件手势识别、MNIST以及Pong任务上实现事件驱动视觉处理的能力。