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首页> 外文期刊>Journal of signal processing systems for signal, image, and video technology >Hardware Specialization in Low-power Sensing Applications to Address Energy and Resilience
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Hardware Specialization in Low-power Sensing Applications to Address Energy and Resilience

机译：低功耗传感应用中的硬件专业化解决能源和弹性

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摘要

This paper explores implications of introducing machine learning capabilities within a hardware-specialized platform for low power embedded sensing applications. Such a platform enables algorithms well suited for analyzing complex sensor signals under strict energy constraints. However, the benefits go further, enabling the effects of errors to be overcome in the presence of hardware faults within the platform. Although errors can result in substantial bit-level perturbations, the approach described views these an alteration on the way that information is encoded within the embedded data. The new information encoding can thus be learned in the form of an error-aware model. The energy implications of hardware-specialized machine-learning kernels are analyzed using a fabricated custom IC, and the hardware-resilience implications are analyzed using an FPGA platform, which permits controllable and randomized injection of logical hardwarefaults.
机译：本文探讨了在低功耗嵌入式传感应用的硬件专用平台内引入机器学习功能的含义。这种平台使算法非常适合在严格的能量约束下分析复杂的传感器信号。但是，好处进一步扩大，可以在平台内出现硬件故障时克服错误的影响。尽管错误可能导致相当大的比特级扰动，但是所描述的方法将这些视为在嵌入式数据中对信息进行编码的方式上的一种改变。因此可以以错误识别模型的形式学习新的信息编码。硬件专用的机器学习内核的能量影响使用制造的定制IC进行了分析，而硬件弹性的影响则通过FPGA平台进行了分析，从而可以对逻辑硬件故障进行可控和随机注入。

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