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

Energy-efficient Continuous Context Sensing on Mobile Phones

With the ever increasing adoption of smartphones worldwide, researchers have found the perfect sensor platform to perform context-based research and to prepare for context-based services to be also deployed for the end-users. However, continuous context sensing imposes a considerable challenge in balancing the energy consumption of the sensors, the accuracy of the recognized context and its latency. After outlining the common characteristics of continuous sensing systems, we present a detailed overview of the state of the art, from sensors sub-systems to context inference algorithms. Then, we present the three main contribution of this thesis. The first approach we present is based on the use of local communications to exchange sensing information with neighboring devices. As proximity, location and environmental information can be obtained from nearby smartphones, we design a protocol for synchronizing the exchanges and fairly distribute the sensing tasks. We show both theoretically and experimentally the reduction in energy needed when the devices can collaborate. The second approach focuses on the way to schedule mobile sensors, optimizing for both the accuracy and energy needs. We formulate the optimal sensing problem as a decision problem and propose a two-tier framework for approximating its solution. The first tier is responsible for segmenting the sensor measurement time series, by fitting various models. The second tier takes care of estimating the optimal sampling, selecting the measurements that contributes the most to the model accuracy. We provide near-optimal heuristics for both tiers and evaluate their performances using environmental sensor data. In the third approach we propose an online algorithm that identifies repeated patterns in time series and produces a compressed symbolic stream. The first symbolic transformation is based on clustering with the raw sensor data. Whereas the next iterations encode repetitive sequences of symbols into new symbols. We define also a metric to evaluate the symbolization methods with regard to their capacity at preserving the systems' states. We also show that the output of symbols can be used directly for various data mining tasks, such as classification or forecasting, without impacting much the accuracy, but greatly reducing the complexity and running time. In addition, we also present an example of application, assessing the user's exposure to air pollutants, which demonstrates the many opportunities to enhance contextual information when fusing sensor data from different sources. On one side we gather fine grained air quality information from mobile sensor deployments and aggregate them with an interpolation model. And, on the other side, we continuously capture the user's context, including location, activity and surrounding air quality. We also present the various models used for fusing all these information in order to produce the exposure estimation

Modeling and Design Techniques for 3-D ICs under Process, Voltage, and Temperature Variations

Three-dimensional (3-D) integration is a promising solution to further enhance the density and performance of modern integrated circuits (ICs). In 3-D ICs, multiple dies (tiers or planes) are vertically stacked. These dies can be designed and fabricated separately. In addition, these dies can be fabricated in different technologies. The effect of different sources of variations on 3-D circuits, consequently, differ from 2-D ICs. As technology scales, these variations significantly affect the performance of circuits. Therefore, it is increasingly important to accurately and efficiently model different sources of variations in 3-D ICs. The process, voltage, and temperature variations in 3-D ICs are investigated in this dissertation. Related modeling and design techniques are proposed to design a robust 3-D IC. Process variations in 3-D ICs are first analyzed. The effect of process variations on synchronization and 3-D clock distribution networks, is carefully studied. A novel statistical model is proposed to describe the timing variation in 3-D clock distribution networks caused by process variations. Based on this model, different topologies of 3-D clock distribution networks are compared in terms of skew variation. A set of guidelines is proposed to design 3-D clock distribution networks with low clock uncertainty. Voltage variations are described by power supply noise. Power supply noise in 3-D ICs is investigated considering different characteristics of potential 3-D power grids in this thesis. A new algorithm is developed to fast analyze the steady-state IR-drop in 3-D power grids. The first droop of power supply noise, also called resonant supply noise, is usually the deepest voltage drop in power distribution networks. The effect of resonant supply noise on 3-D clock distribution networks is investigated. The combined effect of process variations and power supply noise is modeled by skitter consisting of both skew and jitter. A novel statistical model of skitter is proposed. Based on this proposed model and simulation results, a set of guidelines has been proposed to mitigate the negative effect of process and voltage variations on 3-D clock distribution networks. Thermal issues in 3-D ICs are considered by carefully modeling thermal through silicon vias (TTSVs) in this dissertation. TTSVs are vertical vias which do not carry signals, dedicated to facilitate the propagation of heat to reduce the temperature of 3-D ICs. Two analytic models are proposed to describe the heat transfer in 3-D circuits related to TTSVs herein, providing proper closed-form expressions for the thermal resistance of the TTSVs. The effect of different physical and geometric parameters of TTSVs on the temperature of 3-D ICs is analyzed. The proposed models can be used to fast and accurately estimate the temperature to avoid the overuse of TTSVs occupying a large portion of area. A set of models and design techniques is proposed in this dissertation to describe and mitigate the deleterious effects of process, voltage, and temperature variations in 3-D ICs. Due to the continuous shrink in the feature size of transistors, the large number of devices within one circuit, and the high operating frequency, the effect of these variations on the performance of 3-D ICs becomes increasingly significant. Accurately and efficiently estimating and controlling these variations are, consequently, critical tasks for the design of 3-D ICs