水下环境中的无电池机器学习和推理
Towards Battery-Free Machine Learning and Inference in Underwater Environments
摘要：
本文的动机是一个简单的问题：我们能否设计和构建能够在水下环境中进行机器学习和推理的无电池设备？对这一问题的肯定回答将对新一代水下传感和监测应用于环境监测、科学探索和气候/天气预测产生重大影响。为了回答这个问题，我们探讨了在过去十年中在两个领域取得进步的可行性：无电池网络和低功耗机器学习。我们的探索表明，在水下环境中实现无电池推理确实是可能的。我们设计了一种设备，可以从水下声音中获取能量，为超低功耗微控制器和板载传感器供电，使用轻量级深度神经网络对感测到的测量值进行本地推理，并通过反向散射将推理结果传达给接收器。我们在模拟海洋生物声学应用中测试了我们的原型，展示了在没有电池的情况下识别水下动物声音的潜力。通过这种探索，我们强调了使水下无电池推理和机器学习无处不在的挑战和机遇。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
