**SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning** The paper introduces SpiNNaker2, a digital neuromorphic chip designed for scalable machine learning. SpiNNaker2 is an event-based and asynchronous system that can be scaled to involve thousands of chips, making it suitable for large-scale applications. The system's design leverages low-power analog and digital technologies to reduce energy consumption and latency, addressing the computational demands of modern machine learning models. **Key Features:** - **Event-Based and Asynchronous Design:** SpiNNaker2 operates asynchronously, allowing for efficient communication and autonomous execution between cores. - **High-Speed Infrastructure:** It supports high-speed point-to-point communication within chips and system-wide communication between chips through dedicated packet routers. - **Hybrid Models:** The system supports a wide range of models, including artificial neural networks (ANNs), spiking neural networks (SNNs), and hybrid approaches. - **Scalability:** SpiNNaker2 can be scaled from a single chip with 152 ARM Cortex M4F cores to a supercomputer-level system with millions of cores. **Applications:** - **ANNs:** SpiNNaker2 enables energy-efficient training and inference of ANNs, with features like sparse-to-sparse training and deep rewiring to improve efficiency. - **SNNs:** The system supports SNNs, which mimic the brain's sparse communication and reduce energy consumption. It includes event-based backpropagation algorithms like EventProp and biologically-inspired learning rules like e-prop. - **Hybrid Models:** SpiNNaker2 facilitates the integration of ANNs and SNNs, combining the numerical simplicity of ANNs with the energy efficiency of SNNs. **Future Directions:** - **Research and Development:** The 5-million core system in Dresden, Germany, will be available to researchers to explore event-based and asynchronous machine learning models. - **Applications:** Potential applications include energy-efficient large language models, complex brain simulations, probabilistic computing, and distributed drug discovery. **Conclusion:** SpiNNaker2 represents a significant advancement in neuromorphic computing, offering a scalable and energy-efficient solution for machine learning. Its event-based and asynchronous design addresses the limitations of traditional deep learning systems, providing a promising path for future research and applications.**SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning** The paper introduces SpiNNaker2, a digital neuromorphic chip designed for scalable machine learning. SpiNNaker2 is an event-based and asynchronous system that can be scaled to involve thousands of chips, making it suitable for large-scale applications. The system's design leverages low-power analog and digital technologies to reduce energy consumption and latency, addressing the computational demands of modern machine learning models. **Key Features:** - **Event-Based and Asynchronous Design:** SpiNNaker2 operates asynchronously, allowing for efficient communication and autonomous execution between cores. - **High-Speed Infrastructure:** It supports high-speed point-to-point communication within chips and system-wide communication between chips through dedicated packet routers. - **Hybrid Models:** The system supports a wide range of models, including artificial neural networks (ANNs), spiking neural networks (SNNs), and hybrid approaches. - **Scalability:** SpiNNaker2 can be scaled from a single chip with 152 ARM Cortex M4F cores to a supercomputer-level system with millions of cores. **Applications:** - **ANNs:** SpiNNaker2 enables energy-efficient training and inference of ANNs, with features like sparse-to-sparse training and deep rewiring to improve efficiency. - **SNNs:** The system supports SNNs, which mimic the brain's sparse communication and reduce energy consumption. It includes event-based backpropagation algorithms like EventProp and biologically-inspired learning rules like e-prop. - **Hybrid Models:** SpiNNaker2 facilitates the integration of ANNs and SNNs, combining the numerical simplicity of ANNs with the energy efficiency of SNNs. **Future Directions:** - **Research and Development:** The 5-million core system in Dresden, Germany, will be available to researchers to explore event-based and asynchronous machine learning models. - **Applications:** Potential applications include energy-efficient large language models, complex brain simulations, probabilistic computing, and distributed drug discovery. **Conclusion:** SpiNNaker2 represents a significant advancement in neuromorphic computing, offering a scalable and energy-efficient solution for machine learning. Its event-based and asynchronous design addresses the limitations of traditional deep learning systems, providing a promising path for future research and applications.