POETS: A Parallel Cluster Architecture for Spiking Neural Network
POETS: A Parallel Cluster Architecture for Spiking Neural Network
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POETS：尖峰神经网络的并行集群架构
DOI：
10.18178/ijmlc.2021.11.4.1048
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
A. Brown
中科院分区：
文献类型：
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作者：
Mahyar Shahsavari;Jonathan Beaumont;David B. Thomas;A. Brown
尖峰神经网络（SNN）被称为神经形态计算的分支，并且目前用于神经科学应用中以理解和建模生物大脑。SNN还可能用于许多其他应用领域，例如分类、模式识别和自主控制。这项工作提出了一个高度可扩展的硬件平台POETS，并使用它来实现SNN上的一个非常大的数量的并行和可重构的基于FPGA的处理器。目前的系统由48个FPGA组成，提供3072个处理核心和49152个线程。我们用这个硬件来实现多达400万个神经元和1000个突触。与其他类似平台的比较表明，目前的POETS系统比Brian模拟器快20倍，比SpiNNaker快至少两倍。
Spiking Neural Networks (SNNs) are known as a branch of neuromorphic computing and are currently used in neuroscience applications to understand and model the biological brain. SNNs could also potentially be used in many other application domains such as classification, pattern recognition, and autonomous control. This work presents a highly-scalable hardware platform called POETS, and uses it to implement SNN on a very large number of parallel and reconfigurable FPGA-based processors. The current system consists of 48 FPGAs, providing 3072 processing cores and 49152 threads. We use this hardware to implement up to four million neurons with one thousand synapses. Comparison to other similar platforms shows that the current POETS system is twenty times faster than the Brian simulator, and at least two times faster than SpiNNaker.