ECE PhD Dissertation Defense: Raffaele Guida
2020-12-08 14:00 - 15:00 (America/New_York)
PhD Dissertation Defense: Remotely Rechargeable Embedded Platforms for Next Generation IoT Systems in Critical Environments
Raffaele Guida
Location: Teams Meeting
Abstract: In the near future, a new generation of miniaturized, multi-function and smart wireless devices for Internet of Things (IoT) systems, designed for real-time monitoring and with real-time reconfiguration will be deployed in critical and challenging environments, e.g., underwater and inside the human body. These futuristic IoT platforms can now be realized thanks to advances in low-power electronics and wireless communications. However, the need for long-term and reliable power supply, together with the need to support innovative functions, impose new powering requirements that cannot be satisfied by traditional batteries. Batteries have in fact a major impact on the size and lifetime of the device, and often need to be replaced through complex, expensive and non-scalable procedures. For example, powering of Internet of Underwater Things (IoUT) devices in deep water remains one of the main challenges, since these systems are typically powered by batteries that need to be recharged through difficult and expensive operations.
Furthermore, existing medical implants do not provide at once the miniaturized end-to-end sensing-computation-communication-recharging capabilities to implement Implantable Internet of Medical Things (IIoMT) applications.
This dissertation fills the existing research gaps by presenting innovative designs of battery-less devices remotely rechargeable through ultrasonic wireless power transfer. Specifically, two major systems are presented, U-Verse – the first FDA-compliant IIoMT platform packing sensing, computation, communication, and recharging circuits into a penny-scale platform – and the first IoUT battery-less sensor node that can be wirelessly recharged through ultrasonic waves.
U-Verse uses a single miniaturized transducer for data exchange and for wireless charging. To predict U-Verse’s performance, a mathematical model of its charging efficiency is derived and experimentally validated. A matching circuit to maximize the amount of power transferred from the outside is proposed, and the design of a full-fledged cm-scale printed circuit board (PCB) is presented. Extensive experimental evaluation indicates that U-Verse (i) is able to recharge a 330mF and 15F energy storage unit – several orders of magnitude higher than existing work – respectively under 20 and 60 minutes at a depth of 5cm; (ii) achieves stored charge duration of up to 610 and 40 hours in case of battery and supercapacitor energy storage, respectively. Finally, U-Verse is demonstrated through (i) a closed-loop application where a periodic sensing/actuation task sends data via ultrasounds through real porcine meat; and (ii) a real-time reconfigurable pacemaker. As for the underwater sensor node, the architecture of an underwater platform capable of extracting electrical energy from ultrasonic waves is first introduced. Then, the interfacing of the system with an underwater communication unit is illustrated. The design of a prototype where the storage unit is realized with a batch of supercapacitors is also discussed. Experimental results show that the harvested energy is sufficient to provide the sensor node with the power necessary to perform a sensing operation and power a modem for ultrasonic communications. Given the reduced attenuation of ultrasonic waves in water, the proposed approach proves to cover longer distances with less transmission power than alternative solutions. Last, the overall operating efficiency of the system is evaluated.

ECE PhD Dissertation Defense: Sheng Lin
2020-12-08 15:00 - 16:00
PhD Dissertation Defense: Platform-specific Model Compression for Deep Neural Networks with Joint Methods
Sheng Lin
Location: Zoom Link
Abstract: Deep learning has delivered its powerfulness in many application domains, especially in computer vision, natural language processing and speech recognition. As the backbone of deep learning, deep neural networks (DNNs) consist of multiple layers of various types with hundreds to thousands of neurons. Embedded platforms are now becoming essential for deep learning deployment due to their portability, versatility, and energy efficiency. The large model size of DNNs, while providing excellent accuracy, also burdens the hardware platforms with intensive computation and storage. To consider the requirements of specific tasks, many researchers have investigated reducing DNN model size for efficient implementation in hardware devices with reasonable accuracy prediction. However, it lacks a systemic investigation on platform-specific DNN acceleration frameworks.
In this dissertation, we present several software-hardware co-design techniques to speed up the DNN algorithm on specific platforms. At the software level, we present joint model compression techniques for DNN model training and inference with reasonable accuracy performance. At the hardware level, these algorithms and methods are targeting storage reduction, low power consumption, efficient inference, and data security. By using joint methods to optimize different types of networks, the targeted hardware platforms can reduce asymptotic complexity of both computation and storage, making our approach distinguished from existing approaches. First, we present a Fast Fourier Transform-based DNN model for inference phase on embedded platforms. Second, we build a framework for two most commonly used model compression techniques, low-bit linear weight quantization and its combination with different weight pruning methods. Third, we apply quantization techniques for the always-on keyword spotting system and eliminate the energy-consuming ADC with an energy-efficient analog processing circuit. Finally, we propose a federated learning framework to protect user’s data privacy while reducing overall communication cost during the training process.
