SpiNNaker2：用于基于事件和异步机器学习的大规模神经形态系统
SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning
AI总结：
针对现代机器学习在边缘设备的高能耗与延迟问题，本文介绍了数字神经形态芯片SpiNNaker2的运行原理及其大规模异步系统，展示了其在多种神经网络应用中的原型，旨在推动未来基于事件和异步算法的发展。
摘要：
人工神经网络（ANNs）与GPU和TPU等专用硬件加速器的共同进步接管了机器学习研究的许多领域。这种发展伴随着对更大模型和更多数据所需的计算需求的快速增长。同时，基础模型的新兴特性（如上下文学习）为机器学习应用带来了新机遇。然而，此类应用的计算成本是数据中心，更重要的是移动设备和边缘系统中该技术的限制因素。为缓解当代系统的能耗和非平凡延迟，神经形态计算系统通过利用低功耗的模拟和数字技术，深度整合了神经生物学系统的计算原理。SpiNNaker2是为可扩展机器学习开发的数字神经形态芯片。SpiNNaker2的基于事件和异步的设计允许构建涉及数千个芯片的大规模系统。本工作介绍了SpiNNaker2系统的运行原理，概述了新型机器学习应用的原型。这些应用涵盖从ANNs、仿生脉冲神经网络到广义的基于事件的神经网络。通过SpiNNaker2的成功开发和部署，我们旨在促进面向未来机器学习系统的基于事件和异步算法的发展。
英文摘要：
The joint progress of artificial neural networks (ANNs) and domain specific hardware accelerators such as GPUs and TPUs took over many domains of machine learning research. This development is accompanied by a rapid growth of the required computational demands for larger models and more data. Concurrently, emerging properties of foundation models such as in-context learning drive new opportunities for machine learning applications. However, the computational cost of such applications is a limiting factor of the technology in data centers, and more importantly in mobile devices and edge systems. To mediate the energy footprint and non-trivial latency of contemporary systems, neuromorphic computing systems deeply integrate computational principles of neurobiological systems by leveraging low-power analog and digital technologies. SpiNNaker2 is a digital neuromorphic chip developed for scalable machine learning. The event-based and asynchronous design of SpiNNaker2 allows the composition of large-scale systems involving thousands of chips. This work features the operating principles of SpiNNaker2 systems, outlining the prototype of novel machine learning applications. These applications range from ANNs over bio-inspired spiking neural networks to generalized event-based neural networks. With the successful development and deployment of SpiNNaker2, we aim to facilitate the advancement of event-based and asynchronous algorithms for future generations of machine learning systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
