Hardware/Software Co-Design of Ultra-Low Power Biomedical Monitors

Ongoing changes in world demographics and the prevalence of unhealthy lifestyles are imposing a paradigm shift in healthcare delivery. Nowadays, chronic ailments such as cardiovascular diseases, hypertension and diabetes, represent the most common causes of death according to the World Health Organization. It is estimated that 63% of deaths worldwide are directly or indirectly related to these non-communicable diseases (NCDs), and by 2030 it is predicted that the health delivery cost will reach an amount comparable to 75% of the current GDP. In this context, technologies based on Wireless Sensor Nodes (WSNs) effectively alleviate this burden enabling the conception of wearable biomedical monitors composed of one or several devices connected through a Wireless Body Sensor Network (WBSN). Energy efficiency is of paramount importance for these devices, which must operate for prolonged periods of time with a single battery charge. In this thesis I propose a set of hardware/software co-design techniques to drastically increase the energy efficiency of bio-medical monitors. To this end, I jointly explore different alternatives to reduce the required computational effort at the software level while optimizing the power consumption of the processing hardware by employing ultra-low power multi-core architectures that exploit DSP application characteristics. First, at the sensor level, I study the utilization of a heartbeat classifier to perform selective advanced DSP on state-of-the-art ECG bio-medical monitors. To this end, I developed a framework to design and train real-time, lightweight heartbeat neuro-fuzzy classifiers, detail- ing the required optimizations to efficiently execute them on a resource-constrained platform. Then, at the network level I propose a more complex transmission-aware WBSN for activity monitoring that provides different tradeoffs between classification accuracy and transmission volume. In this work, I study the combination of a minimal set of WSNs with a smartphone, and propose two classification schemes that trade accuracy for transmission volume. The proposed method can achieve accuracies ranging from 88% to 97% and can save up to 86% of wireless transmissions, outperforming the state-of-the-art alternatives. Second, I propose a synchronization-based low-power multi-core architecture for bio-signal processing. I introduce a hardware/software synchronization mechanism that allows to achieve high energy efficiency while parallelizing the execution of multi-channel DSP applications. Then, I generalize the methodology to support bio-signal processing applications with an arbitrarily high degree of parallelism. Due to the benefits of SIMD execution and software pipelining, the architecture can reduce its power consumption by up 38% when compared to an equivalent low-power single-core alternative. Finally, I focused on the optimization of the multi-core memory subsystem, which is the major contributor to the overall system power consumption. First I considered a hybrid memory subsystem featuring a small reliable partition that can operate at ultra-low voltage enabling low-power buffering of data and obtaining up to 50% energy savings. Second, I explore a two-level memory hierarchy based on non-volatile memories (NVM) that allows for aggressive fine-grained power gating enabled by emerging low-power NVM technologies and monolithic 3D integration. Experimental results show that, by adopting this memory hierarchy, power consumption can be reduced by 5.42x in the DSP stage

SMART SENSOR AND TRACKING SYSTEM FOR UNDERGROUND MINING

The thesis predominantly discusses a smart sensor and tracking system for under- ground mining, as developed by the author. The tracking system is developed by two steps, the rst of which involves nding an e cient way to measure the distance, and the second of which involves localizing the positions of each miner in real-time. For the rst step, a Received Signal Strength Indicator (RSSI) is used to measure the distance between two points by indicating the amount of energy lost during the transmission. Due to environmental and human factors, errors exist when using RSSI to measure distance. Three methods are taken to reduce the error: Gaussian distribution, statistical average and preset points. It can be observed that the average error between actual distance and measured distance is only 0.1145 meters using the proposed model. In regards to the localization, the "3-point localization method" is considered rst. With the proposed method, the result of the localization is improved by 0.6 meters, as compared to the "2-point localization method". The transmission method for the project is then discussed. After comparing sev- eral transmission protocols in the market, ZigBee was chosen for the signal trans- mission. With the Zigbee protocol, up to 65000 nodes can be connected, which are suitable for many miners using the system at the same time. The power supply for the ZigBee protocol is only 1mW for each unit, thus potentially saving a great amount of energy during the transmission. To render the tracking system more powerful, two smart sensors are installed: an MQ-2 sensor and a temperature sensor. The MQ-2 sensor is used to detect the harmful gas and smoke. In the event that the sensor's detected value is beyond the threshold, it will provide a warning for the supervisor on the ground

Energy-efficient embedded machine learning algorithms for smart sensing systems

Embedded autonomous electronic systems are required in numerous application domains such as Internet of Things (IoT), wearable devices, and biomedical systems. Embedded electronic systems usually host sensors, and each sensor hosts multiple input channels (e.g., tactile, vision), tightly coupled to the electronic computing unit (ECU). The ECU extracts information by often employing sophisticated methods, e.g., Machine Learning. However, embedding Machine Learning algorithms poses essential challenges in terms of hardware resources and energy consumption because of: 1) the high amount of data to be processed; 2) computationally demanding methods. Leveraging on the trade-off between quality requirements versus computational complexity and time latency could reduce the system complexity without affecting the performance. The objectives of the thesis are to develop: 1) energy-efficient arithmetic circuits outperforming state of the art solutions for embedded machine learning algorithms, 2) an energy-efficient embedded electronic system for the \u201celectronic-skin\u201d (e-skin) application. As such, this thesis exploits two main approaches: Approximate Computing: In recent years, the approximate computing paradigm became a significant major field of research since it is able to enhance the energy efficiency and performance of digital systems. \u201cApproximate Computing\u201d(AC) turned out to be a practical approach to trade accuracy for better power, latency, and size . AC targets error-resilient applications and offers promising benefits by conserving some resources. Usually, approximate results are acceptable for many applications, e.g., tactile data processing,image processing , and data mining ; thus, it is highly recommended to take advantage of energy reduction with minimal variation in performance . In our work, we developed two approximate multipliers: 1) the first one is called \u201cMETA\u201d multiplier and is based on the Error Tolerant Adder (ETA), 2) the second one is called \u201cApproximate Baugh-Wooley(BW)\u201d multiplier where the approximations are implemented in the generation of the partial products. We showed that the proposed approximate arithmetic circuits could achieve a relevant reduction in power consumption and time delay around 80.4% and 24%, respectively, with respect to the exact BW multiplier. Next, to prove the feasibility of AC in real world applications, we explored the approximate multipliers on a case study as the e-skin application. The e-skin application is defined as multiple sensing components, including 1) structural materials, 2) signal processing, 3) data acquisition, and 4) data processing. Particularly, processing the originated data from the e-skin into low or high-level information is the main problem to be addressed by the embedded electronic system. Many studies have shown that Machine Learning is a promising approach in processing tactile data when classifying input touch modalities. In our work, we proposed a methodology for evaluating the behavior of the system when introducing approximate arithmetic circuits in the main stages (i.e., signal and data processing stages) of the system. Based on the proposed methodology, we first implemented the approximate multipliers on the low-pass Finite Impulse Response (FIR) filter in the signal processing stage of the application. We noticed that the FIR filter based on (Approx-BW) outperforms state of the art solutions, while respecting the tradeoff between accuracy and power consumption, with an SNR degradation of 1.39dB. Second, we implemented approximate adders and multipliers respectively into the Coordinate Rotational Digital Computer (CORDIC) and the Singular Value Decomposition (SVD) circuits; since CORDIC and SVD take a significant part of the computationally expensive Machine Learning algorithms employed in tactile data processing. We showed benefits of up to 21% and 19% in power reduction at the cost of less than 5% accuracy loss for CORDIC and SVD circuits when scaling the number of approximated bits. 2) Parallel Computing Platforms (PCP): Exploiting parallel architectures for near-threshold computing based on multi-core clusters is a promising approach to improve the performance of smart sensing systems. In our work, we exploited a novel computing platform embedding a Parallel Ultra Low Power processor (PULP), called \u201cMr. Wolf,\u201d for the implementation of Machine Learning (ML) algorithms for touch modalities classification. First, we tested the ML algorithms at the software level; for RGB images as a case study and tactile dataset, we achieved accuracy respectively equal to 97% and 83.5%. After validating the effectiveness of the ML algorithm at the software level, we performed the on-board classification of two touch modalities, demonstrating the promising use of Mr. Wolf for smart sensing systems. Moreover, we proposed a memory management strategy for storing the needed amount of trained tensors (i.e., 50 trained tensors for each class) in the on-chip memory. We evaluated the execution cycles for Mr. Wolf using a single core, 2 cores, and 3 cores, taking advantage of the benefits of the parallelization. We presented a comparison with the popular low power ARM Cortex-M4F microcontroller employed, usually for battery-operated devices. We showed that the ML algorithm on the proposed platform runs 3.7 times faster than ARM Cortex M4F (STM32F40), consuming only 28 mW. The proposed platform achieves 15 7 better energy efficiency than the classification done on the STM32F40, consuming 81mJ per classification and 150 pJ per operation

Growth and Oxidation of Graphene and Two-Dimensional Materials for Flexible Electronic Applications

The non-volatile storage of information is becoming increasingly important in our data-driven society. Limitations in conventional devices are driving the research and development of incorporating new materials into conventional device architectures to improve performance, as well as developing an array of emerging memory technologies based on entirely new physical processes. The discovery of graphene allowed for developing new approaches to these problems, both itself and as part of the larger, and ever-expanding family of 2D materials. In this thesis the growth and oxidation of these materials is investigated for implementing into such devices, exploiting some of the unique properties of 2D materials including atomic thinness, mechanical flexibility and tune-ability through chemical modification - to meet some challenges facing the community. This begins with the growth of graphene by chemical vapour deposition for a high quality flexible electrode material, followed by oxidation of graphene for use in resistive memory devices. The theme of oxidation is then extended to another 2D material, HfS2, which is selectively oxidised for use as high-k dielectric in Van der Waals heterostructures for FETs and resistive memory devices. Lastly, a technique for fabrication of graphene-based devices directly on the copper growth substrate is demonstrated for use in flexible devices for sensing touch and humidity

Ultra-low Voltage Digital Circuits and Extreme Temperature Electronics Design

Certain applications require digital electronics to operate under extreme conditions e.g., large swings in ambient temperature, very low supply voltage, high radiation. Such applications include sensor networks, wearable electronics, unmanned aerial vehicles, spacecraft, and energyharvesting systems. This dissertation splits into two projects that study digital electronics supplied by ultra-low voltages and build an electronic system for extreme temperatures. The first project introduces techniques that improve circuit reliability at deep subthreshold voltages as well as determine the minimum required supply voltage. These techniques address digital electronic design at several levels: the physical process, gate design, and system architecture. This dissertation analyzes a silicon-on-insulator process, Schmitt-trigger gate design, and asynchronous logic at supply voltages lower than 100 millivolts. The second project describes construction of a sensor digital controller for the lunar environment. Parts of the digital controller are an asynchronous 8031 microprocessor that is compatible with synchronous logic, memory with error detection and correction, and a robust network interface. The digitial sensor ASIC is fabricated on a silicon-germanium process and built with cells optimized for extreme temperatures

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology

EEG-based Brain-computer interfaces (BCI) are facing grant challenges in their real-world applications. The technical difficulties in developing truly wearable multi-modal BCI systems that are capable of making reliable real-time prediction of users’ cognitive states under dynamic real-life situations may appear at times almost insurmountable. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report our attempt to develop a pervasive on-line BCI system by employing state-of-art technologies such as multi-tier fog and cloud computing, semantic Linked Data search and adaptive prediction/classification models. To verify our approach, we implement a pilot system using wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end fog servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end cloud servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch and the UCSD Movement Disorder Center to use our system in real-life personal stress and in-home Parkinson’s disease patient monitoring experiments. We shall proceed to develop a necessary BCI ontology and add automatic semantic annotation and progressive model refinement capability to our system

Trustworthy Wireless Personal Area Networks

In the Internet of Things (IoT), everyday objects are equipped with the ability to compute and communicate. These smart things have invaded the lives of everyday people, being constantly carried or worn on our bodies, and entering into our homes, our healthcare, and beyond. This has given rise to wireless networks of smart, connected, always-on, personal things that are constantly around us, and have unfettered access to our most personal data as well as all of the other devices that we own and encounter throughout our day. It should, therefore, come as no surprise that our personal devices and data are frequent targets of ever-present threats. Securing these devices and networks, however, is challenging. In this dissertation, we outline three critical problems in the context of Wireless Personal Area Networks (WPANs) and present our solutions to these problems. First, I present our Trusted I/O solution (BASTION-SGX) for protecting sensitive user data transferred between wirelessly connected (Bluetooth) devices. This work shows how in-transit data can be protected from privileged threats, such as a compromised OS, on commodity systems. I present insights into the Bluetooth architecture, Intel’s Software Guard Extensions (SGX), and how a Trusted I/O solution can be engineered on commodity devices equipped with SGX. Second, I present our work on AMULET and how we successfully built a wearable health hub that can run multiple health applications, provide strong security properties, and operate on a single charge for weeks or even months at a time. I present the design and evaluation of our highly efficient event-driven programming model, the design of our low-power operating system, and developer tools for profiling ultra-low-power applications at compile time. Third, I present a new approach (VIA) that helps devices at the center of WPANs (e.g., smartphones) to verify the authenticity of interactions with other devices. This work builds on past work in anomaly detection techniques and shows how these techniques can be applied to Bluetooth network traffic. Specifically, we show how to create normality models based on fine- and course-grained insights from network traffic, which can be used to verify the authenticity of future interactions

The Boston University Photonics Center annual report 2016-2017

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2016-2017 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This has undoubtedly been the Photonics Center’s best year since I became Director 10 years ago. In the following pages, you will see highlights of the Center’s activities in the past year, including more than 100 notable scholarly publications in the leading journals in our field, and the attraction of more than 22 million dollars in new research grants/contracts. Last year I had the honor to lead an international search for the first recipient of the Moustakas Endowed Professorship in Optics and Photonics, in collaboration with ECE Department Chair Clem Karl. This professorship honors the Center’s most impactful scholar and one of the Center’s founding visionaries, Professor Theodore Moustakas. We are delighted to haveawarded this professorship to Professor Ji-Xin Cheng, who joined our faculty this year.The past year also marked the launch of Boston University’s Neurophotonics Center, which will be allied closely with the Photonics Center. Leading that Center will be a distinguished new faculty member, Professor David Boas. David and I are together leading a new Neurophotonics NSF Research Traineeship Program that will provide $3M to promote graduate traineeships in this emerging new field. We had a busy summer hosting NSF Sites for Research Experiences for Undergraduates, Research Experiences for Teachers, and the BU Student Satellite Program. As a community, we emphasized the theme of “Optics of Cancer Imaging” at our annual symposium, hosted by Darren Roblyer. We entered a five-year second phase of NSF funding in our Industry/University Collaborative Research Center on Biophotonic Sensors and Systems, which has become the centerpiece of our translational biophotonics program. That I/UCRC continues to focus on advancing the health care and medical device industries