Spiking neural network (SNN) has attracted extensive attention in large-scale image processing tasks. To obtain higher computing efficiency, the development of hardware architecture suitable for SNN computing has become a hot research topic. However, the existing hardware of spike neurons still has high computational complexity and they do not perform well enough on complicated datasets, and the neuromorphic system cannot support SNNs with different convolutional topologies, resulting in low efficiency of the system. To address the above problems, an optimized leaky integrated-and-fire (LIF) neuron called EPC-LIF and a neuromorphic hardware acceleration system (ELIF-NHAS) are designed and implemented based on the field-programmable gate array (Xilinx Kintex-7). First, the classical LIF neuron is designed using the optimization method of extended prediction correction (EPC), which can reduce the computation complexity and hardware resources with a maximum frequency of 439.95 MHz. The ELIF-NHAS is constructed and optimized with parallel and pipeline techniques for effectively running SNNs, working with a maximum frequency of 135.6 MHz. Then, the genetic algorithm is applied to adjust the membrane threshold of neurons for further improving the accuracy of SNNs. Furthermore, the ELIF-NHAS can support different SNNs with multilayer perceptron and convolutional neural network topologies (called SCNN), including traditional, depth-separate, and residual convolutions. The accuracy of multilayer SCNNs can achieve 99.10%, 90.29%, and 82.15% on MNIST, Fashion-MNIST, and SVHN datasets, respectively; and the speed and energy consumption achieve 1.21 ms/image and 1.19 mJ/image. Compared with existing systems, the ELIF-NHAS is more suitable for the deployment and inference of SNNs with higher speed and lower consumption.

中文翻译：

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用于支持具有 MLP 和 CNN 拓扑的脉冲神经网络的神经形态硬件的实现和优化

尖峰神经网络（SNN）在大规模图像处理任务中引起了广泛关注。为了获得更高的计算效率，开发适用于SNN计算的硬件架构成为研究热点。然而，现有的尖峰神经元硬件仍然具有较高的计算复杂度，并且它们在复杂数据集上的表现不够好，并且神经形态系统无法支持具有不同卷积拓扑结构的 SNN，导致系统效率低下。为了解决上述问题，基于现场可编程门阵列（Xilinx Kintex-7）设计并实现了一种称为 EPC-LIF 的优化泄漏集成和发射（LIF）神经元和神经形态硬件加速系统（ELIF-NHAS） ). 第一的，经典LIF神经元采用扩展预测校正（EPC）的优化方法设计，可降低计算复杂度和硬件资源，最大频率为439.95 MHz。ELIF-NHAS 采用并行和流水线技术构建和优化，可有效运行 SNN，最大工作频率为 135.6 MHz。然后，应用遗传算法调整神经元的膜阈值，进一步提高SNNs的准确性。此外，ELIF-NHAS 可以支持具有多层感知器和卷积神经网络拓扑（称为 SCNN）的不同 SNN，包括传统、深度分离和残差卷积。多层 SCNN 在 MNIST、Fashion-MNIST 和 SVHN 数据集上的准确率分别可以达到 99.10%、90.29% 和 82.15%；速度和能耗分别达到1.21 ms/image和1.19 mJ/image。与现有系统相比，ELIF-NHAS更适合速度更快、消耗更低的SNNs的部署和推理。