论文标题
FPGA实施尖峰神经网络的概率尖峰传播
Probabilistic spike propagation for FPGA implementation of spiking neural networks
论文作者
论文摘要
峰值神经网络的评估需要取得大量的突触权重，以更新突触后神经元。这限制了并行性，并成为硬件的瓶颈。 我们提出了一种基于权重的概率解释的尖峰传播方法，从而减少了内存访问和更新。我们研究将随机性引入尖峰处理的效果，并在基准网络上显示这可以对识别准确性的影响最小。 我们在Xilinx Zynq平台上为MNIST和CIFAR10数据集的完全连接和卷积网络的精确度提出了一个体系结构和权衡。
Evaluation of spiking neural networks requires fetching a large number of synaptic weights to update postsynaptic neurons. This limits parallelism and becomes a bottleneck for hardware. We present an approach for spike propagation based on a probabilistic interpretation of weights, thus reducing memory accesses and updates. We study the effects of introducing randomness into the spike processing, and show on benchmark networks that this can be done with minimal impact on the recognition accuracy. We present an architecture and the trade-offs in accuracy on fully connected and convolutional networks for the MNIST and CIFAR10 datasets on the Xilinx Zynq platform.
