Artificial intelligence (AI) is gaining success and importance in many different tasks. The growing pervasiveness and complexity of AI systems push researchers towards developing dedicated hardware accelerators. Spiking Neural Networks (SNN) represent a promising solution in this sense since they implement models that are more suitable for a reliable hardware design. Moreover, from a neuroscience perspective, they better emulate a human brain. This work presents the development of a hardware accelerator for an SNN, with off-line training, applied to an image recognition task, using the MNIST as the target dataset. Many techniques are used to minimize the area and to maximize the performance, such as the replacement of the multiplication operation with simple bit shifts and the minimization of the time spent on inactive spikes, useless for the update of neurons' internal state. The design targets a Xilinx Artix-7 FPGA, using in total around the 40% of the available hardware resources and reducing the classification time by three orders of magnitude, with a small 4.5% impact on the accuracy, if compared to its software, full precision counterpart.

该研究开发了一种基于 Xilinx Artix-7 FPGA 的硬件加速器，用于高性能 Spiking Neural Networks 推理，评估了该加速器的推理能力，并与现有的加速器进行比较。

Spiker：面向脉冲神经网络的FPGA优化硬件加速