Hardware/Software Co-Design and Reliability Analysis of Ultra-Low Power Biomedical Devices

Smart health monitoring devices, known as Wireless Body Sensor Nodes (WBSN) are transforming today's health-care landscape, shifting it from traditional hospital-based methods toward more personalized approaches. Their demand is ever-increasing in the modern world, where an overwhelming majority of deaths are caused by chronic cardiovascular diseases. WBSNs provide low-cost, unobtrusive and wearable solutions for continuous health-monitoring. These small autonomous devices are capable of acquiring, processing and wirelessly transmitting biological signals to medical personnel, typically relying on a limited on-board power source. Since WBSNs are desired to operate over prolonged periods, producing potentially critical data, their reliability and energy efficiency are of paramount importance. State-of-the-Art (SoA) WBSNs are able to process on-board the sensed bio-signals, like Electrocardiograms (ECG), transmitting only compact sets of clinically-relevant features instead of the entire acquired signal. This scenario moves the energy bottleneck of such devices from the transmission link to the computation-intensive Bio-Signal Processing (BSP) segment. To improve their energy efficiency, in this thesis, I propose strategies ranging from architectural to technology levels. First, I explore the applicability of a relaxed-reliability computing paradigm resulting from ultra-low voltage operations. I begin by modeling memory failures ensuing from voltage over-scaling and fabrication variability-related issues, studying their effects at the application level. To this end, I introduce novel data significance-based protection approaches for countering failures in embedded memories, showcasing up to 20% reduction in the energy budget compared to existing solutions. In addition, I devise lightweight hardware- and software-level error monitoring solutions that cope with memory failures, when unprotected. Experimental results demonstrate that these strategies ensure minimal performance degradation, achieving up to 24% energy savings with respect to the SoA. In the second part of the thesis, I employ novel approaches featuring heterogeneous WBSNs with a reconfigurable accelerator and emerging memory technologies. I commence by performing a study to identify the computationally intensive segments of BSP algorithms that can potentially be accelerated on a reconfigurable fabric. I adopt a top-down approach to do so, extracting kernels from the high-level application code, eventually optimizing and mapping them for execution on a time- and resource-shared Coarse Grain Reconfigurable Array (CGRA). I prove the effectiveness of the proposed platform by exhibiting up to 11x performance improvement and 37% energy savings over the SoA. Then, I combine relaxed-reliability computation and CGRA-based WBSNs, with my experiments exhibiting as much as 70% reduction in energy consumption compared to the current SoA, while assuring safe system operation. I conclude this work by exploring the integration of emerging Resistive Random Access Memory (ReRAM) technologies to traditional WBSN platforms, aimed at reducing their ever-increasing leakage energy consumption. In this regard, I report the memory-level endurance challenges faced due to aging and propose lightweight solutions for prolonged device usage. Obtained results highlight up to 65% increase in energy efficiency with respect to the SoA, including the overheads required for extended device functionality.

Adaptive wake and sleep detection for wearable systems

Walter Karlen

Sleep problems and disorders have a serious impact on human health and wellbeing. The rising costs for treating sleep-related chronic diseases in industrialized countries demands efficient prevention. Low-cost, wearable sleep / wake detection systems which give feedback on the wearer's "sleep performance" are a promising approach to reduce the risk of developing serious sleep disorders and fatigue. Not all bio-medical signals that are useful for sleep / wake discrimination can be easily recorded with wearable systems. Sensors often need to be placed in an obtrusive location on the body or cannot be efficiently embedded into a wearable frame. Furthermore, wearable systems have limited computational and energetic resources, which restrict the choice of sensors and algorithms for online processing and classification. Since wearable systems are used outside the laboratory, the recorded signals tend to be corrupted with additional noise that influences the precision of classification algorithms. In this thesis we present the research on a wearable sleep / wake classifier system that relies on cardiorespiratory (ECG and respiratory effort) and activity recordings and that works autonomously with minimal user interaction. This research included the selection of optimal signals and sensors, the development of a custom-tailored hardware demonstrator with embedded classification algorithms, and the realization of experiments in real-world environments for the customization and validation of the system. The processing and classification of the signals were based on Fourier transformations and artificial neural networks that are efficiently implementable into digital signal controllers. Literature analysis and empiric measurements revealed that cardiorespiratory signals are more promising for a wearable sleep / wake classification than clinically used signals such as brain potentials. The experiments conducted during this thesis showed that inter-subject differences within the recorded physiological signals make it difficult to design a sleep / wake classification model that can generalize to a group of subjects. This problem was addressed in two ways: First by adding features from another signal to the classifier, that is, measuring the behavioral quiescence during sleep using accelerometers. Conducted research on different feature extraction methods from accelerometer data showed that this data generalizes well for distinct subjects in the study group. In addition, research on user-adaptation methods was conducted. Behavioral sleep and wake measures, notably the measurement of reactivity and activity, were developed to build up a priori knowledge that was used to adapt the classification algorithm automatically to new situations. This thesis demonstrates the design and development of a low-cost, wearable hardware and embedded software for on-line sleep / wake discrimination. The proposed automatic user-adaptive classifier is advantageous compared to previously suggested classification methods that generalize over multiple subjects, because it can take changes in the wearer's physiology and sleep / wake behavior into account without adjustment from a human expert. The results of this thesis contribute to the development of smart, wearable, bio-physiological monitoring systems which require a high degree of autonomy and have only low computational resources available. We believe that the proposed sleep / wake classification system is a first promising step toward a context-aware system for sleep management, sleep disorder prevention, and reduction of fatigue.

EPFL2009

Hardware / Software Architectural and Technological Exploration for Energy-Efficient and Reliable Biomedical Devices

Loris Gérard Duch

Nowadays, the ubiquity of smart appliances in our everyday lives is increasingly strengthening the links between humans and machines. Beyond making our lives easier and more convenient, smart devices are now playing an important role in personalized healthcare delivery. This technological breakthrough is particularly relevant in a world where population aging and unhealthy habits have made non-communicable diseases the first leading cause of death worldwide according to international public health organizations. In this context, smart health monitoring systems termed Wireless Body Sensor Nodes (WBSNs), represent a paradigm shift in the healthcare landscape by greatly lowering the cost of long-term monitoring of chronic diseases, as well as improving patients' lifestyles. WBSNs are able to autonomously acquire biological signals and embed on-node Digital Signal Processing (DSP) capabilities to deliver clinically-accurate health diagnoses in real-time, even outside of a hospital environment. Energy efficiency and reliability are fundamental requirements for WBSNs, since they must operate for extended periods of time, while relying on compact batteries. These constraints, in turn, impose carefully designed hardware and software architectures for hosting the execution of complex biomedical applications. In this thesis, I develop and explore novel solutions at the architectural and technological level of the integrated circuit design domain, to enhance the energy efficiency and reliability of current WBSNs. Firstly, following a top-down approach driven by the characteristics of biomedical algorithms, I perform an architectural exploration of a heterogeneous and reconfigurable computing platform devoted to bio-signal analysis. By interfacing a shared Coarse-Grained Reconfigurable Array (CGRA) accelerator, this domain-specific platform can achieve higher performance and energy savings, beyond the capabilities offered by a baseline multi-processor system. More precisely, I propose three CGRA architectures, each contributing differently to the maximization of the application parallelization. The proposed Single, Multi and Interleaved-Datapath CGRA designs allow the developed platform to achieve substantial energy savings of up to 37%, when executing complex biomedical applications, with respect to a multi-core-only platform. Secondly, I investigate how the modeling of technology reliability issues in logic and memory components can be exploited to adequately adjust the frequency and supply voltage of a circuit, with the aim of optimizing its computing performance and energy efficiency. To this end, I propose a novel framework for workload-dependent Bias Temperature Instability (BTI) impact analysis on biomedical application results quality. Remarkably, the framework is able to determine the range of safe circuit operating frequencies without introducing worst-case guard bands. Experiments highlight the possibility to safely raise the frequency up to 101% above the maximum obtained with the classical static timing analysis. Finally, through the study of several well-known biomedical algorithms, I propose an approach allowing energy savings by dynamically and unequally protecting an under-powered data memory in a new way compared to regular error protection schemes. This solution relies on the Dynamic eRror compEnsation And Masking (DREAM) technique that reduces by approximately 21% the energy consumed by traditional error correction codes.

EPFL2018

Adaptive wake and sleep detection for wearable systems

Walter Karlen

EPFL2009

Hardware / Software Architectural and Technological Exploration for Energy-Efficient and Reliable Biomedical Devices

Loris Gérard Duch

EPFL2018