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Energy-efficient multipath ring network for heterogeneous clustered neuronal arrays

机译:异构簇神经元阵列的节能多径环网

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Simulating large spiking neural networks with a high level of realism in a FPGA requires efficient network architectures that satisfy both the resource and interconnect constraints, as well as the changes in traffic patterns due to learning processes. In this paper, we propose a dataflow architecture based on a multipath ring topology that offers traffic shaping capabilities, and high energy-efficiency for the neuron-to-neuron communications.
机译:在FPGA中模拟具有高度真实性的大型尖峰神经网络需要有效的网络架构,该架构既要满足资源和互连的约束,又要满足因学习过程而引起的流量模式的变化。在本文中,我们提出了一种基于多径环形拓扑的数据流体系结构,该体系结构提供了流量整形功能,并为神经元至神经元通信提供了高能效。

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