论文标题
FPGA实施简化的尖峰神经网络
FPGA Implementation of Simplified Spiking Neural Network
论文作者
论文摘要
尖峰神经网络（SNN）是接近生物神经系统的第三代人工神经网络（ANN）。近年来，SNN在机器人技术和嵌入式应用领域变得很流行，因此，必须探索其实时和节能实施。 SNN比其前任更强大，因为它们编码时间信息并使用生物学上合理的可塑性规则。在本文中，描述了使用FPGA体系结构的更简单，更有效的SNN模型。提出的模型在Xilinx Virtex 6 FPGA上进行了验证，并分析了一个完全连接的网络，该网络由800个神经元和12,544个突触组成。
Spiking Neural Networks (SNN) are third-generation Artificial Neural Networks (ANN) which are close to the biological neural system. In recent years SNN has become popular in the area of robotics and embedded applications, therefore, it has become imperative to explore its real-time and energy-efficient implementations. SNNs are more powerful than their predecessors because they encode temporal information and use biologically plausible plasticity rules. In this paper, a simpler and computationally efficient SNN model using FPGA architecture is described. The proposed model is validated on a Xilinx Virtex 6 FPGA and analyzes a fully connected network which consists of 800 neurons and 12,544 synapses in real-time.
