FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices

Deep neural networks show great potential as solutions to many sensing application problems, but their excessive resource demand slows down execution time, pausing a serious impediment to deployment on low-end devices. To address this challenge, recent literature focused on compressing neural network size to improve performance. We show that changing neural network size does not proportionally affect performance attributes of interest, such as execution time. Rather, extreme run-time nonlinearities exist over the network configuration space. Hence, we propose a novel framework, called FastDeepIoT, that uncovers the non-linear relation between neural network structure and execution time, then exploits that understanding to find network configurations that significantly improve the trade-off between execution time and accuracy on mobile and embedded devices. FastDeepIoT makes two key contributions. First, FastDeepIoT automatically learns an accurate and highly interpretable execution time model for deep neural networks on the target device. This is done without prior knowledge of either the hardware specifications or the detailed implementation of the used deep learning library. Second, FastDeepIoT informs a compression algorithm how to minimize execution time on the profiled device without impacting accuracy. We evaluate FastDeepIoT using three different sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus. FastDeepIoT further reduces the neural network execution time by $48\%$ to $78\%$ and energy consumption by $37\%$ to $69\%$ compared with the state-of-the-art compression algorithms.Comment: Accepted by SenSys '1

Prediction of larynx function using multichannel surface EMG classification

Total laryngectomy (TL) affects critical functions such as swallowing, coughing and speaking. An artificial, bioengineered larynx (ABL), operated via myoelectric signals, may improve quality of life for TL patients. To evaluate the efficacy of using surface electromyography (sEMG) as a control signal to predict instances of swallowing, coughing and speaking, sEMG was recorded from submental, intercostal and diaphragm muscles. The cohort included TL and control participants. Swallowing, coughing, speaking and movement actions were recorded, and a range of classifiers were investigated for prediction of these actions. Our algorithm achieved F1-scores of 76.0 ± 4.4 % (swallows), 93.8 ± 2.8 % (coughs) and 70.5 ± 5.4 % (speech) for controls, and 67.7 ± 4.4 % (swallows), 71.0 ± 9.1 % (coughs) and 78.0 ± 3.8 % (speech) for TLs, using a random forest (RF) classifier. 75.1 ± 6.9 % of swallows were detected within 500 ms of onset in the controls, and 63.1 ± 6.1 % in TLs. sEMG can be used to predict critical larynx movements, although a viable ABL requires improvements. Results are particularly encouraging as they encompass a TL cohort. An ABL could alleviate many challenges faced by laryngectomees. This study represents a promising step toward realising such a device

Miniaturized RF Components With A Novel Tunable Engineered Substrate For Wireless Communication Systems

There is an increasing demand for reliable sensor system capable of remote sensing and measuring interesting data. Although large communication range can be achieved, active wireless communication systems are still suboptimal in longtime applications due to their harmful battery supply. Inductively coupled passive devices, with the advantages in safe long-term implanting, structural simplicity, small fabrication footprint and low-cost production, are preferred in chronic monitoring, but little work has been done to optimize the performance of these systems, especially under some design constraints. The model and optimization of an inductively coupled wireless pressure sensor system is presented in this dissertation. With MEMS and semiconductor technology, the pressure sensor is designed as a miniaturized LC resonant circuit operating in 402MHz within a small footprint of 3.2 mm by 3.2 mm. An optimization approach is conducted to analyze inductive as well as pressure sensitivity. With mutually dependent geometrical parameters and performance related RF characteristics considered in the full optimization of the system, the applied design of this experiment method can reduce the large number of combined groups of values in fractional simulations with a focus on a few performance related factors. The second task of this research is to improve the limited working range of the sensing system. A half-active wireless communication system is studied as an alternative solution to this problem. Wireless power harvesting circuits and auxiliarydata-acquisition circuits are integrated in the system for long distance communication. However, physical size of system also becomes large with the added circuits. The challenges of designing compact wireless communication system are proposed to be solved in this dissertation. With the requirements of multi-band and multi-function in wireless communication systems with improved performance and reduced size, development of tunable miniaturized RF components are a promising solution to fulfill the trend. Many technologies have been investigated and applied to develop tunable devices including MEMS and semiconductor varactors, ferroelectric capacitors, and magnetically tunable inductors with ferromagnetic materials, etc. However, the tunability of reported devices using the above technologies is directly dependent on the individual design configurations, which limits the design flexibility and broader application. A unique solution is to design arbitrary tunable RF components using an engineered substrate with an embedded patterned permalloy (Py) thin film which was developed for the first time in this dissertation. With high and current-dependent permeability, an engineered substrate embedded with Py thin film is a promising and flexible approach to design compact frequency-agile RF devices. Py thin film is patterned into slim bars on an engineered substrate to improve its ferromagnetic resonant frequency (FMR) for RF and mmwave applications. Miniaturized RF components are first developed with the proposed engineered magneto-dielectric substrate in this dissertation. Permeability tunable smart substrate was also developed by integrating an array of DC bias lines to provide a tuning path of Py patterns. The design principles and factors affecting the characteristics of the engineered substrate have been fully analyzed. Design efficacy of the developed tunable substrate has been demonstrated with implemented components including a patch antenna, a phase shifter, a bandpass filter, and a three-port bandpass filtering balun. The proposed engineered substrate is feasible in implementing arbitrary RF and microwave devices with improved tuning capability and design flexibility

Event-driven Middleware for Body and Ambient Sensor Applications

Continuing development of on-body and ambient sensors has led to a vast increase in sensor-based assistance and monitoring solutions. A growing range of modular sensors, and the necessity of running multiple applications on the sensor information, has led to an equally extensive increase in efforts for system development. In this work, we present an event-driven middleware for on-body and ambient sensor networks allowing multiple applications to define information types of their interest in a publish/subscribe manner. Incoming sensor data is hereby transformed into the required data representation which lifts the burden of adapting the application with respect to the connected sensors off the developer's shoulders. Furthermore, an unsupervised on-the-fly reloading of transformation rules from a remote server allows the system's adaptation to future applications and sensors at run-time as well as reducing the number of connected sensors. Open communication channels distribute sensor information to all interested applications. In addition to that, application-specific event channels are introduced that provide tailor-made information retrieval as well as control over the dissemination of critical information. The system is evaluated based on an Android implementation with transformation rules implemented as OSGi bundles that are retrieved from a remote web server. Evaluation shows a low impact of running the middleware and the transformation rules on a phone and highlights the reduced energy consumption by having fewer sensors serving multiple applications. It also points out the behavior and limits of the open and application-specific event channels with respect to CPU utilization, delivery ratio, and memory usage. In addition to the middleware approach, four (preventive) health care applications are presented. They take advantage of the mediation between sensors and applications and highlight the system's capabilities. By connecting body sensors for monitoring physical and physiological parameters as well as ambient sensors for retrieving information about user presence and interactions with the environment, full-fledged health monitoring examples for monitoring a user throughout the day are presented. Vital parameters are gathered from commercially available biosensors and the mediator device running both the middleware and the application is an off-the-shelf smart phone. For gaining information about a user's physical activity, custom-built body and ambient sensors are presented and deployed

生体信号による動作分類とリハビリテーションへの応用

兵庫県立大学大学院201

Novas tecnologias aplicadas à saúde : desenvolvimento de sistemas dinâmicos : conceitos, aplicações e utilização de técnicas inteligentes e regulação

Faculdade UnB Gama (FGA