Neural Networks (NNs) are increasingly used in the last decade in several demanding applications, such as object detection and classification, autonomous driving, etc. Among different computing platforms for implementing NNs, FPGAs have multiple advantages due to design flexibility and high performance-to-watt ratio. Moreover, approximation techniques, such as quantization, have been introduced, which reduce the computational and storage requirements, thus enabling the integration of larger NNs into FPGA devices. On the other hand, FPGAs are sensitive to radiation-induced Single Event Upsets (SEUs). In this work, we perform an in-depth reliability analysis in an FPGA-based Binarized Fully Connected Neural Network (BNN) accelerator running a statistical fault injection campaign. The BNN benchmark has been produced by FINN, an open-source framework that provides an end-to-end flow from abstract level to design, making it easy to design customized FPGA NN accelerators, while it also supports various approximation techniques. The campaign includes the injection of faults in the configuration memory of a state-of-the-art Xilinx Ultrascale+ FPGA running the BNN, as well an exhaustive fault injection in the user flip flops. We have analyzed the fault injection results characterizing the SEU vulnerability of the circuit per network layer, per clock cycle, and register. In general, the results show that the BNNs are inherently resilient to soft errors, since a low portion of SEUs in the configuration memory and the flip flops, cause system crashes or misclassification errors.

翻译：神经网络在过去十年中被广泛应用于目标检测与分类、自动驾驶等要求苛刻的应用场景。在实现神经网络的多种计算平台中，FPGA凭借设计灵活性和高能效比具备多重优势。此外，量化等近似技术的引入降低了计算与存储需求，使得在FPGA器件中集成更大规模神经网络成为可能。然而，FPGA对辐射引发的单粒子翻转（SEU）具有敏感性。本研究针对基于FPGA的二值化全连接神经网络（BNN）加速器开展了深度可靠性分析，通过统计故障注入实验进行评估。该BNN基准测试由开源框架FINN生成——该框架提供从抽象层级到设计的端到端流程，不仅简化了定制化FPGA神经网络加速器的设计，还支持多种近似技术。实验包括对运行BNN的先进Xilinx Ultrascale+ FPGA配置存储器进行故障注入，以及对用户触发器进行穷举式故障注入。我们从网络层、时钟周期和寄存器三个维度分析了故障注入结果，表征了电路各部分的SEU脆弱性。实验结果表明，BNN对软错误具有天然鲁棒性：配置存储器和触发器中造成系统崩溃或分类错误的SEU仅占极低比例。