SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning

SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning

9 Jan 2024 | Hector A. Gonzalez, Jiaxin Huang, Florian Kelber, Khaleelulla Khan Nazeer, Tim Langer, Chen Liu, Matthias Lohrmann, Amirhossein Rostami, Mark Schöne, Bernhard Vogginger, Timo C. Wunderlich, Yexin Yan, Mahmoud Akl, Christian Mayr
**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.
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