论文标题
在水下环境中迈向无电池的机器学习和推断
Towards Battery-Free Machine Learning and Inference in Underwater Environments
论文作者
论文摘要
本文是由一个简单的问题激励的:我们可以在水下环境中设计和制造能够机器学习和推理的无电池设备吗?对这个问题的肯定答案将对新一代的水下感应和监测应用程序具有重大影响,以进行环境监测,科学探索以及气候/天气预测。 为了回答这个问题,我们探索了过去十年中两个领域中桥接进步的可行性:无电池网络和低功率机学习。我们的探索表明,确实可以在水下环境中实现无电池推断。我们设计了一种可以从水下声音中收集能量的设备,为超低功率微控制器和板载传感器供电,使用轻量级的深神经网络对感测测量进行局部推断,并通过反向分散的接收器传达推理结果。我们在模拟的海洋生物声学应用中测试了原型,证明了识别没有电池的水下动物声音的潜力。通过这次探索,我们强调了制作无水下电池推理和机器学习无处不在的挑战和机会。
This paper is motivated by a simple question: Can we design and build battery-free devices capable of machine learning and inference in underwater environments? An affirmative answer to this question would have significant implications for a new generation of underwater sensing and monitoring applications for environmental monitoring, scientific exploration, and climate/weather prediction. To answer this question, we explore the feasibility of bridging advances from the past decade in two fields: battery-free networking and low-power machine learning. Our exploration demonstrates that it is indeed possible to enable battery-free inference in underwater environments. We designed a device that can harvest energy from underwater sound, power up an ultra-low-power microcontroller and on-board sensor, perform local inference on sensed measurements using a lightweight Deep Neural Network, and communicate the inference result via backscatter to a receiver. We tested our prototype in an emulated marine bioacoustics application, demonstrating the potential to recognize underwater animal sounds without batteries. Through this exploration, we highlight the challenges and opportunities for making underwater battery-free inference and machine learning ubiquitous.