Predictive maintenance systems face a rich set of constraints along dimensions such as latency, throughput, physical size, monetary cost, as well as energy and power consumption.

To meet performance requirements, predictive maintenance systems require specialized compute units (i.e., accelerators) in addition to conventional processor cores. Unfortunately, size and cost constraints commonly result in developers being forced into selecting System-on-Chip (SoC) platforms that do not have sufficient resources to fully accelerate all performance-critical functions — in essence raising the challenging question of how to optimally distribute the available resources across accelerators. This work introduces the Resource-Constrained Accelerator Selection (RCS) methodology, which identifies near-optimal multi-accelerator configurations for predictive maintenance applications.

RCS takes a library of resource-scalable accelerator architectures as input and then selects the combination of accelerator configurations that minimizes end-to-end latency. We find that enabling RCS for typical predictive maintenance applications requires a resource-scalable Fast Fourier Transform (FFT) accelerator and propose ScaleFFT to fill this gap.

We apply RCS and ScaleFFT to a collection of edge computing applications with different sensor bandwidths and find that they reduce end-to-end latency by 2.4$\times$ on average for a 256K-point FFT compared to a state-of-the-art configuration that only accelerates the machine learning algorithm. Moreover, we demonstrate that RCS enables real-world gains in oil well and train track monitoring systems.

预测性维护系统在延迟、吞吐量、物理尺寸、货币成本以及能源和功耗等方面面临着丰富的约束。

为了满足性能要求，预测性维护系统除了传统的处理器内核外还需要专门的计算单元（即加速器）。不幸的是，尺寸和成本限制通常会导致开发人员被迫选择没有足够资源来完全加速所有性能关键功能的片上系统 (SoC) 平台——本质上提出了如何优化分布的挑战性问题跨加速器的可用资源。这项工作介绍了资源受限加速器选择 (RCS)方法，该方法可为预测性维护应用程序确定近乎最优的多加速器配置。

RCS 将资源可扩展加速器架构库作为输入，然后选择可最大限度减少端到端延迟的加速器配置组合。我们发现，为典型的预测性维护应用启用 RCS 需要资源可扩展的快速傅立叶变换 (FFT) 加速器，并提出ScaleFFT来填补这一空白。

我们将 RCS 和 ScaleFFT 应用于一组具有不同传感器带宽的边缘计算应用程序，发现它们将端到端延迟减少了 2.4$\times$与仅加速机器学习算法的最先进配置相比，平均 256K 点 FFT。此外，我们证明 RCS 可以在油井和火车轨道监控系统中实现现实世界的收益。