Intelligence Beyond the Edge: Inference on Intermittent Embedded Systems
Intelligence Beyond the Edge: Inference on Intermittent Embedded Systems
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DOI：
10.1145/3297858.3304011
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发表时间：
2018-09
期刊：
影响因子：
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通讯作者：
Graham Gobieski;Nathan Beckmann;Brandon Lucia
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文献类型：
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作者：
Graham Gobieski;Nathan Beckmann;Brandon Lucia
能量收集技术为未来的物联网应用提供了一个有前途的平台。然而，由于这些设备中的通信非常昂贵，应用程序将需要“超越边缘”的推理，以避免将宝贵的能量浪费在无意义的通信上。我们表明，应用程序的性能是高度敏感的推理精度。不幸的是，准确的推理需要大量的计算和内存，而能量收集系统受到严重的资源限制。此外，能量收集系统间歇性地运行，经常发生电力故障，破坏了结果并阻碍了前进。本文克服了这些挑战，在能量收集系统上首次全面演示了DNN推理。我们设计并实现了SONIC，这是一个具有专门支持DNN推理的智能感知软件系统。SONIC引入了循环延续，这是一种新技术，可以显著降低保证DNN推理等循环繁重代码正确间歇执行的成本。为了构建一个完整的系统，我们进一步介绍了GENESIS，一种自动压缩网络以优化平衡推理精度和能量的工具，以及TAILS，它利用某些微控制器中可用的SIMD硬件来提高能源效率。SONIC和TAILS都保证正确的间歇执行，而无需在不同的电源系统中进行任何手动调整或性能损失。在商用微控制器上的三个神经网络中，SONIC和TAILS分别将推理能量降低了6.9倍和12.2倍。
Energy-harvesting technology provides a promising platform for future IoT applications. However, since communication is very expensive in these devices, applications will require inference "beyond the edge" to avoid wasting precious energy on pointless communication. We show that application performance is highly sensitive to inference accuracy. Unfortunately, accurate inference requires large amounts of computation and memory, and energy-harvesting systems are severely resource-constrained. Moreover, energy-harvesting systems operate intermittently, suffering frequent power failures that corrupt results and impede forward progress. This paper overcomes these challenges to present the first full-scale demonstration of DNN inference on an energy-harvesting system. We design and implement SONIC, an intermittence-aware software system with specialized support for DNN inference. SONIC introduces loop continuation, a new technique that dramatically reduces the cost of guaranteeing correct intermittent execution for loop-heavy code like DNN inference. To build a complete system, we further present GENESIS, a tool that automatically compresses networks to optimally balance inference accuracy and energy, and TAILS, which exploits SIMD hardware available in some microcontrollers to improve energy efficiency. Both SONIC & TAILS guarantee correct intermittent execution without any hand-tuning or performance loss across different power systems. Across three neural networks on a commercially available microcontroller, SONIC & TAILS reduce inference energy by 6.9× and 12.2×, respectively, over the state-of-the-art.