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.

中文翻译：

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分析 SRAM FPGA 上二值化神经网络的单事件翻转漏洞

神经网络 (NN) 在过去十年中越来越多地应用于一些要求较高的应用中，例如目标检测和分类、自动驾驶等。在用于实现 NN 的不同计算平台中，FPGA 由于设计灵活性和高性能而具有多种优势-瓦特比。此外，还引入了量化等近似技术，降低了计算和存储要求，从而能够将更大的神经网络集成到 FPGA 设备中。另一方面，FPGA 对辐射引起的单粒子扰动 (SEU) 很敏感。在这项工作中，我们在运行统计故障注入活动的基于 FPGA 的二值化全连接神经网络 (BNN) 加速器中执行深入的可靠性分析。 BNN 基准测试由 FINN 制作，FINN 是一个开源框架，提供从抽象层到设计的端到端流程，使设计定制 FPGA NN 加速器变得容易，同时它还支持各种近似技术。该活动包括在运行 BNN 的最先进 Xilinx Ultrascale+ FPGA 的配置存储器中注入故障，以及在用户触发器中进行详尽的故障注入。我们分析了故障注入结果，表征了每个网络层、每个时钟周期和寄存器电路的 SEU 漏洞。总的来说，结果表明 BNN 本质上对软错误具有弹性，因为配置存储器和触发器中的 SEU 比例较低，会导致系统崩溃或误分类错误。