MLWiNS: Resource Constrained Mobile Data Analytics Assisted by the Wireless Edge

移动电话和物联网(IoT)设备上收集的数据越来越多。用户感兴趣的是分析这些数据以提取可操作的信息，例如，从高分辨率手机图片中识别感兴趣的对象。这种数据分析的最先进技术是通过深度学习，这利用了模拟人脑功能的复杂软件算法。然而，深度学习算法太复杂了，无法在电池有限的小型移动设备上运行。替代方案，即向移动基站发送数据，其中深度学习算法可以在强大的服务器上执行，消耗了太多的带宽。该项目寻求设计新的方法在传输前压缩数据，从而降低带宽成本，同时仍允许在基站对数据进行分析。与为再现原始图像而优化的现有数据压缩方法不同，该项目将使用深度学习本身来压缩数据，以便只保留后续分析所需的关键数据部分。由此产生的基于深度学习的压缩算法将足够简单，可以在移动设备上运行，同时大幅减少需要传输到移动基站进行分析的数据量，而不会显著影响分析性能。拟议的研究将为移动设备用户提供更大的能力和功能，延长电池寿命，并更有效地共享用于分析任务的无线频谱。该项目还设想了一个多管齐下的努力，旨在扩展到感兴趣的社区，教育和培训下一代机器学习和无线专业人员，在K-12、本科生和研究生水平，并扩大代表不足的少数群体的参与。该项目寻求通过训练在移动设备上执行的低复杂性压缩器深度神经网络(DNN)，从数据中学习“有分析意识的”压缩方案，并实现一系列传输速率和分析精度目标。作为第一步，将开发高效的DNN修剪技术来最小化DNN的复杂性，同时保持一个或一组分析任务的速率精度效率。下一步，为了有效地适应变化的无线信道条件，该项目将寻求设计能够在不同的传输速率和计算复杂性下运行的自适应DNN架构。例如，当无线信道质量下降时，建议的压缩方案将能够快速降低传输速率作为响应，同时确保相同的分析精度，但代价是移动设备上的更大计算能力。此外，将开发利用建议的自适应DNN架构的无线信道分配和调度策略，以优化服务器的整体分析准确性。通过对各种无线协议(包括用于LTE(长期演进)和毫米波信道的协议)的详细模拟研究，将展示建议方法在为移动设备节省总电池寿命方面的优势。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Increasing amounts of data are being collected on mobile telephones and internet-of-things (IoT) devices. Users are interested in analyzing this data to extract actionable information, for example, identifying objects of interest from high-resolution mobile phone pictures. The state-of-the-art technique for such data analysis is via deep learning which makes use of sophisticated software algorithms modeled on the functioning of the human brain. Deep learning algorithms are, however, too complex to run on small, battery constrained mobile devices. The alternative, i.e., transmitting data to the mobile base station where the deep learning algorithm can be executed on a powerful server, consumes too much bandwidth. This project seeks to devise new methods to compress data before transmission, thus reducing bandwidth costs while still allowing for the data to be analyzed at the base station. Departing from existing data compression methods optimized for reproducing the original images, the project will use deep learning itself to compress the data in a fashion that only keeps the critical parts of data necessary for subsequent analysis. The resulting deep learning based compression algorithms will be simple enough to run on mobile devices while drastically reducing the amount of data that needs to be transmitted to mobile base stations for analysis, without significantly compromising the analysis performance. The proposed research will provide greater capability and functionality to mobile device users, enable extended battery lifetimes, and more efficient sharing of the wireless spectrum for analytics tasks. The project also envisions a multi-pronged effort aimed at outreach to communities of interest, educating and training the next generation of machine learning and wireless professionals at the K-12, undergraduate and graduate levels, and broadening participation of under-represented minority groups.The project seeks to learn “analytics-aware” compression schemes from data by training low-complexity compressor deep neural networks (DNNs) that execute on mobile devices and achieve a range of transmission rate and analytics accuracy targets. As a first step, efficient DNN pruning techniques will be developed to minimize the DNN complexity, while maintaining the rate-accuracy efficiency for one or a collection of analytics tasks. Next, to efficiently adapt to varying wireless channel conditions, the project will seek to design adaptive DNN architectures that can operate at variable transmission rates and computational complexities. For instance, when the wireless channel quality drops, the proposed compression scheme will be able to quickly reduce transmission rate in response while ensuring the same analytics accuracy, but at the cost of greater computational power on the mobile device. Further, wireless channel allocation and scheduling policies that leverage the proposed adaptive DNN architectures will be developed to optimize the overall analytics accuracy at the server. The benefits of the proposed approach in terms of total battery life savings for the mobile device will be demonstrated using detailed simulation studies of various wireless protocols including those used for LTE (Long Term Evolution) and mmWave channels.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.