论文标题
Brainscales-2与杂交可塑性加速神经形态系统
The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity
论文作者
论文摘要
自从电子组件开始信息处理以来，神经系统一直是计算原始组织组织的隐喻。如今，脑启发的计算涵盖了一类方法，从使用新型的纳米设备进行计算到研究大规模神经形态体系结构，例如Truenorth，Spinnaker，Brainscales，Tianjic和Loihi。尽管实现细节有所不同，但尖峰神经网络（有时称为第三代神经网络）是用于使用此类系统建模计算的常见抽象。在这里，我们描述了Brainscales神经形态体系结构的第二代，强调了该体系结构启用的应用程序。它结合了自定义模拟加速器核心核心，该核心支持生物启发的尖峰神经网络原始图的加速物理仿真以及紧密耦合的数字处理器和数字事件 - 路口网络。
Since the beginning of information processing by electronic components, the nervous system has served as a metaphor for the organization of computational primitives. Brain-inspired computing today encompasses a class of approaches ranging from using novel nano-devices for computation to research into large-scale neuromorphic architectures, such as TrueNorth, SpiNNaker, BrainScaleS, Tianjic, and Loihi. While implementation details differ, spiking neural networks - sometimes referred to as the third generation of neural networks - are the common abstraction used to model computation with such systems. Here we describe the second generation of the BrainScaleS neuromorphic architecture, emphasizing applications enabled by this architecture. It combines a custom analog accelerator core supporting the accelerated physical emulation of bio-inspired spiking neural network primitives with a tightly coupled digital processor and a digital event-routing network.
