标题： SFATTI：用于时间任务驱动推理的脉冲FPGA加速器——以MNIST为例 显示英文标题

标题： SFATTI: Spiking FPGA Accelerator for Temporal Task-driven Inference -- A Case Study on MNIST

摘要： 硬件加速器对于在边缘应用如图像识别中实现低延迟和高能效的推理至关重要。 脉冲神经网络（SNNs）由于其事件驱动和时间稀疏的特性而特别有前景，这使得它们非常适合低功耗现场可编程门阵列（FPGA）部署。 本文探讨使用开源的Spiker+框架在MNIST数据集上生成优化的SNN加速器用于手写数字识别。 Spiker+支持对网络拓扑、神经元模型和量化进行高级别规范，自动生成可部署的HDL。 我们评估了多种配置并分析了与边缘计算约束相关的权衡。 显示英文摘要

摘要： Hardware accelerators are essential for achieving low-latency, energy-efficient inference in edge applications like image recognition. Spiking Neural Networks (SNNs) are particularly promising due to their event-driven and temporally sparse nature, making them well-suited for low-power Field Programmable Gate Array (FPGA)-based deployment. This paper explores using the open-source Spiker+ framework to generate optimized SNNs accelerators for handwritten digit recognition on the MNIST dataset. Spiker+ enables high-level specification of network topologies, neuron models, and quantization, automatically generating deployable HDL. We evaluate multiple configurations and analyze trade-offs relevant to edge computing constraints.