Lorenzo Keller

Miguel de Prado, Jing Su, Rabia Saeed et al.

Next generation of embedded Information and Communication Technology (ICT) systems are collaborative systems able to perform autonomous tasks. The remarkable expansion of the embedded ICT market, together with the rise and breakthroughs of Artificial Intelligence (AI), have put the focus on the Edge as it stands as one of the keys for the next technological revolution: the seamless integration of AI in our daily life. However, training and deployment of custom AI solutions on embedded devices require a fine-grained integration of data, algorithms, and tools to achieve high accuracy. Such integration requires a high level of expertise that becomes a real bottleneck for small and medium enterprises wanting to deploy AI solutions on the Edge which, ultimately, slows down the adoption of AI on daily-life applications. In this work, we present a modular AI pipeline as an integrating framework to bring data, algorithms, and deployment tools together. By removing the integration barriers and lowering the required expertise, we can interconnect the different stages of tools and provide a modular end-to-end development of AI products for embedded devices. Our AI pipeline consists of four modular main steps: i) data ingestion, ii) model training, iii) deployment optimization and, iv) the IoT hub integration. To show the effectiveness of our pipeline, we provide examples of different AI applications during each of the steps. Besides, we integrate our deployment framework, LPDNN, into the AI pipeline and present its lightweight architecture and deployment capabilities for embedded devices. Finally, we demonstrate the results of the AI pipeline by showing the deployment of several AI applications such as keyword spotting, image classification and object detection on a set of well-known embedded platforms, where LPDNN consistently outperforms all other popular deployment frameworks.

Anh Le, Lorenzo Keller, Hulya Seferoglu et al.

We consider a group of mobile users, within proximity of each other, who are interested in watching the same online video at roughly the same time. The common practice today is that each user downloads the video independently on her mobile device using her own cellular connection, which wastes access bandwidth and may also lead to poor video quality. We propose a novel cooperative system where each mobile device uses simultaneously two network interfaces: (i) the cellular to connect to the video server and download parts of the video and (ii) WiFi to connect locally to all other devices in the group and exchange those parts. Devices cooperate to efficiently utilize all network resources and are able to adapt to varying wireless network conditions. In the local WiFi network, we exploit overhearing, and we further combine it with network coding. The end result is savings in cellular bandwidth and improved user experience (faster download) by a factor on the order up to the group size. We follow a complete approach, from theory to practice. First, we formulate the problem using a network utility maximization (NUM) framework, decompose the problem, and provide a distributed solution. Then, based on the structure of the NUM solution, we design a modular system called MicroCast and we implement it as an Android application. We provide both simulation results of the NUM solution and experimental evaluation of MicroCast on a testbed consisting of Android phones. We demonstrate that the proposed approach brings significant performance benefits without battery penalty.