Many ground-level and space systems require reliability testing before their deployment, since they are increasingly susceptible to transient and permanent faults. Such process must be accurate, controllable, generic, cheap, and fast. Even though fault injection at gate-level is often the most appropriate solution when one seeks for accuracy and controllability, it is very time-consuming. Considering that, this work proposes a hybrid fault injection framework that automatically switches between RTL and gate-level simulation modes. By using a complex 8-issue VLIW processor as case-study, we show that the injection process can be accelerated by more than \(10\times \) for transient faults and almost 2 times for permanent faults over conventional injectors, while maintaining gate-level accuracy and controllability. The proposed framework is generic, so that faults can be injected into any arbitrary circuit, which is demonstrated by also injecting faults in a neural network and achieving a speedup of more than \(30\times \).

中文翻译：

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快速，准确的混合故障注入平台，用于瞬态和永久性故障

许多地面和空间系统在部署之前都需要进行可靠性测试，因为它们越来越容易受到瞬态和永久性故障的影响。这样的过程必须准确，可控，通用，便宜且快速。尽管在寻求准确性和可控性时，通常在门级进行故障注入是最合适的解决方案，但这还是非常耗时的。考虑到这一点，这项工作提出了一种混合故障注入框架，该框架可在RTL和门级仿真模式之间自动切换。通过使用复杂的8个问题的VLIW处理器进行案例研究，我们表明注入过程可以加速超过\（10 \ times \）相对于常规喷油器而言，它的瞬态故障几乎是永久性故障的2倍，同时保持了门级精度和可控性。所提出的框架是通用的，因此可以将故障注入到任意电路中，这也可以通过将故障注入神经网络并实现大于（（30 \ times））的加速来证明。