Abstract
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.
Abstract (translated)
在这项工作中，我们介绍了HiAER-Spike，这是一个模块化、可重构的事件驱动神经形态计算平台，旨在执行高达1.6亿个神经元和400亿个突触的大规模脉冲神经网络——这大约是老鼠大脑神经元数量的两倍，并且运行速度超过实时。该系统在加州大学圣地亚哥分校超级计算机中心组装而成，它包含了一个专门为大规模并行处理而优化、采用层次化地址事件路由（HiAER）的软硬件堆栈，在提高内存效率的同时支持网络存储和执行。这种架构能够高效地应对稀疏连接和稀疏活动，适用于边缘计算和云计算中的稳健且低延迟的事件驱动推理。我们还提供了一个Python编程接口给HiAER-Spike，该接口不依赖于硬件细节，并能简化用户在配置和执行一般脉冲神经网络时的操作复杂性，仅需遵守有限的拓扑约束。通过一个网页门户，这个系统被广泛地社区成员所轻松访问并使用。 接下来，我们概述了软硬件堆栈，解释了背后的设计原则，并展示了该系统的部分能力，同时邀请更广泛的神经形态计算社区提供反馈意见。文中还提供了实例展示HiAER-Spike在事件驱动视觉（基准CIFAR-10）、DVS基于事件的手势识别、MNIST任务和Pong游戏等领域的应用能力。
URL
https://arxiv.org/abs/2602.18072
