Wearable internet of things (IoT) are transforming diverse healthcare applications including rehabilitation, vital symptom monitoring, and activity recognition. However, the small form-factor of wearable devices constrains the battery capacity and the operating lifetime, thus requiring frequent recharging or battery replacements. Frequent recharging and battery replacement leads to lower quality of service and user satisfaction. Harvesting energy from ambient sources to augment the battery has emerged as an effective solution to improving its operating lifetime. However, ambient energy sources are highly stochastic, making energy management challenging. Prior approaches typically use point predictions for estimating future energy and do not explicitly account for the uncertainty. In strong contrast to prior approaches, this paper presents a conformal predictionbased method for future energy harvest that provides small uncertainty regions with provable coverage guarantees (true output is within the uncertainty region). The uncertainty regions over energy harvest are then leveraged in an energy management algorithm that employs Monte Carlo sampling to evaluate the quality of multiple decisions with varying energy harvests. The decisions are then combined using a lightweight machine learning model to make an energy management decision that is close to the optimal. Experiments on two diverse real-world datasets with about 10 users show that conformal prediction achieves more than 90% coverage with tight prediction intervals; and the energy management algorithm produces decisions that are on average within 2 J of an optimal Oracle, thus showing its effectiveness in improving the quality of service.

Achieving good health and well-being through lower mortality rates of non-communicable diseases and early warning of health risks are key goals of United Nations (UN). Wearable internet of things (IoT) are one of the most promising technology to achieve these goals through their ubiquitous monitoring of key health indicators and in-situ data processing. However, small form-factor of wearable devices constrains the battery capacity, thus requiring frequent recharging or battery replacements, which lowers their adoption rate and benefits. Augmentation of battery energy by scavenging ambient sources, such as light, is a promising solution to improve operating lifetime of IoT devices. However, ambient energy sources are highly uncertain, making energy management (EM) challenging. To handle these challenges, this paper presents a novel uncertainty-aware EM approach. First, we develop a conformal prediction-based method for future energy harvest (EH) that provides small uncertainty regions with provable coverage guarantees (true output vector is within the region). The EH uncertainty regions are then leveraged in an EM algorithm that uses overhead-aware sampling to evaluate the quality of multiple decisions with varying EH before making a decision using a lightweight machine learning model. Experiments on two diverse real-world datasets with 10 users show that conformal prediction achieves more than 90% coverage with tight prediction intervals; and the EM algorithm produces decisions that are, on average, within 2 Joules of an optimal Oracle.

Wearable devices that have low-power sensors, processors, and communication capabilities are gaining wide adoption in several health applications. The machine learning algorithms on these devices assume that data from all sensors are available during runtime. However, data from one or more sensors may be unavailable due to energy or communication challenges. This loss of sensor data can result in accuracy degradation of the application. Prior approaches to handle missing data, such as generative models or training multiple classifiers for each combination of missing sensors are not suitable for low-energy wearable devices due to their high overhead at runtime. In contrast to prior approaches, we present an energy-efficient approach, referred to as Sensor-Aware iMputation (SAM), to accurately impute missing data at runtime and recover application accuracy. SAM first uses unsupervised clustering to obtain clusters of similar sensor data patterns. Next, it learns inter-relationship between clusters to obtain imputation patterns for each combination of clusters using a principled sensor-aware search algorithm. Using sensor data for clustering before choosing imputation patterns ensures that the imputation is aware of sensor data observations. Experiments on seven diverse wearable sensor-based time-series datasets demonstrate that SAM is able to maintain accuracy within 5% of the baseline with no missing data when one sensor is missing. We also compare SAM against generative adversarial imputation networks (GAIN), transformers, and k-nearest neighbor methods. Results show that SAM outperforms all three approaches on average by more than 25% when two sensors are missing with negligible overhead compared to the baseline.

Wearable and Internet of Things (IoT) devices are becoming popular in several applications, such as health monitoring, wide area sensing, and digital agriculture. These devices are energy-constrained due to limited battery capacities. As such, IoT devices harvest energy from the environment and manage it to prolong operation of the system. Stochastic nature of ambient energy, coupled with small battery sizes may lead to insufficient energy for obtaining data from all sensors. As a result, sensors either have to be duty cycled or subsampled to meet the energy budget. However, machine learning (ML) models for these applications are typically trained with the assumption that data from all sensors are available, leading to loss in accuracy. To overcome this, we propose a novel approach that combines data imputation with energy management (EM). Data imputation aims to substitute missing data with appropriate values so that complete sensor data are available for application processing, while EM makes energy budget decisions on the devices. We use the energy budget to obtain complete data from as many sensors as possible and turn off other sensors instead of duty cycling all sensors. Then, we use a low-overhead imputation technique for unavailable sensors and use them in ML models. Evaluations with six diverse datasets show that the proposed EM with imputation approach achieves 25%–55% higher accuracy when compared to duty cycling or subsampling without using additional energy.

Internet of Things (IoT) devices have the potential to enable a wide range of applications, including smart health and agriculture. However, they are limited by their small battery capacities. Utilizing energy harvesting is a promising approach to augment the battery life of IoT devices. However, relying solely on harvested energy is insufficient due to the stochastic nature of ambient sources. Predicting and accounting for uncertainty in the energy harvest (EH) is critical for optimal energy management (EM) in wearable IoT devices. This article proposes a two-step uncertainty-aware EH prediction and management framework for wearable IoT devices. First, the framework employs an energy-efficient conformal prediction (CP) method to predict future EH and construct prediction intervals. Contrasting to prior CP approaches, we propose constructing the prediction intervals using a combination of residuals from previous hours and days. Second, the framework proposes a near-optimal EM approach that utilizes a rollout algorithm. The rollout algorithm efficiently simulates various energy allocation trajectories as a function of predicted EH bounds. Using results from the rollout, the proposed approach constructs energy allocation bounds that maximize application utility (quality of service) with a high probability. Evaluations using real-world energy data from ARAS and Mannheim datasets show that the proposed CP for EH prediction provides 93% coverage probability with an average width of 9.5 J and 1.9 J, respectively. Moreover, EM using the rollout algorithm provides energy allocation decisions that are within 1.9–2.9 J of the optimal with minimal overhead.

Human activity recognition (HAR) and, more broadly, activities of daily life recognition using wearable devices have the potential to transform a number of applications, including mobile healthcare, smart homes, and fitness monitoring. Recent approaches for HAR use multiple sensors on various locations on the body to achieve higher accuracy for complex activities. While multiple sensors increase the accuracy, they are also susceptible to reliability issues when one or more sensors are unable to provide data to the application due to sensor malfunction, user error, or energy limitations. Training multiple activity classifiers that use a subset of sensors is not desirable, since it may lead to reduced accuracy for applications. To handle these limitations, we propose a novel generative approach that recovers the missing data of sensors using data available from other sensors. The recovered data are then used to seamlessly classify activities. Experiments using three publicly available activity datasets show that with data missing from one sensor, the proposed approach achieves accuracy that is within 10% of the accuracy with no missing data. Moreover, implementation on a wearable device prototype shows that the proposed approach takes about 1.5 ms for recovering data in the w-HAR dataset, which results in an energy consumption of 606 μJ. The low-energy consumption ensures that SensorGAN is suitable for effectively recovering data in tinyML applications on energy-constrained devices.

Wearable and internet of things (IoT) devices are transforming a number of high-impact applications. Machine learning (ML) algorithms on wearable devices assume that data from all sensors is available at runtime. However, one or more sensors may be unavailable at runtime due to malfunction, energy constraints or communication challenges. Loss of sensor data can potentially lead to severe degradation in application accuracy and quality of service. Commonly employed generative ML methods to recover missing data are not suitable for resource-constrained wearables because they incur significant memory, execution time, and energy overhead at runtime. In contrast to prior methods, this paper presents a novel search-based accuracy-preserving imputation (AIM) algorithm that obtains most likely imputation patterns of sensor data for each missing data scenario via offline analytics. Specifically, for each missing data condition, we store the most likely recovery patterns which preserve ML classifier-based application accuracy in a look up table and use it appropriately at runtime. The key insight behind AIM is that we do not need exact recovery of the missing data as long as the ML classifier-based application accuracy (e.g., health assessment) is preserved. To further improve the overall effectiveness of AIM, we train the ML classifiers to be robust to small errors in data recovery. Experiments on four diverse wearable sensor based time-series benchmarks demonstrate that AIM is able to maintain accuracy within 5% of the baseline with no missing data when one sensor is missing, and improves the overall accuracy by 15% compared to a state-of-the-art baseline. AIM achieves this improvement with negligible energy consumption overhead.

Human activity recognition (HAR) has increased in recent years due to its applications in mobile health monitoring, activity recognition, and patient rehabilitation. The typical approach is training a HAR classifier offline with known users and then using the same classifier for new users. However, the accuracy for new users can be low with this approach if their activity patterns are different than those in the training data. At the same time, training from scratch for new users is not feasible for mobile applications due to the high computational cost and training time. To address this issue, we propose a HAR transfer learning framework with two components. First, a representational analysis reveals common features that can transfer across users and user-specific features that need to be customized. Using this insight, we transfer the reusable portion of the offline classifier to new users and fine-tune only the rest. Our experiments with five datasets show up to 43% accuracy improvement and 66% training time reduction when compared to the baseline without using transfer learning. Furthermore, measurements on the hardware platform reveal that the power and energy consumption decreased by 43% and 68%, respectively, while achieving the same or higher accuracy as training from scratch. Our code is released for reproducibility. 1

Human activity recognition (HAR) is an important component in a number of health applications, including rehabilitation, Parkinson’s disease, daily activity monitoring, and fitness monitoring. State-of-the-art HAR approaches use multiple sensors on the body to accurately identify activities at runtime. These approaches typically assume that data from all sensors are available for runtime activity recognition. However, data from one or more sensors may be unavailable due to malfunction, energy constraints, or communication challenges between the sensors. Missing data can lead to significant degradation in the accuracy, thus affecting quality of service to users. A common approach for handling missing data is to train classifiers or sensor data recovery algorithms for each combination of missing sensors. However, this results in significant memory and energy overhead on resource-constrained wearable devices. In strong contrast to prior approaches, this paper presents a clustering-based approach (CIM) to impute missing data at runtime. We first define a set of possible clusters and representative data patterns for each sensor in HAR. Then, we create and store a mapping between clusters across sensors. At runtime, when data from a sensor are missing, we utilize the stored mapping table to obtain most likely cluster for the missing sensor. The representative window for the identified cluster is then used as imputation to perform activity classification. We also provide a method to obtain imputation-aware activity prediction sets to handle uncertainty in data when using imputation. Experiments on three HAR datasets show that CIM achieves accuracy within 10% of a baseline without missing data for one missing sensor when providing single activity labels. The accuracy gap drops to less than 1% with imputation-aware classification. Measurements on a low-power processor show that CIM achieves close to 100% energy savings compared to state-of-the-art generative approaches.

Internet of things (IoT) devices are popular in several high-impact applications such as mobile healthcare and digital agriculture. However, IoT devices have limited operating lifetime due to their small form factor. Harvesting energy from ambient sources is an effective method to supplement the battery. Energy harvesting necessitates development of energy management policies to manage the harvested energy. Designing optimal policies for energy management is challenging for two key reasons: (1) ambient energy sources are highly stochastic; therefore, energy management policies must consider the associated uncertainty; (2) energy management policies must consider future energy availability while making decisions to ensure that sufficient energy is available when there is no ambient energy. Prior approaches typically consider energy in the immediate future (e.g., 1 hour) and do not account for the uncertainty in future energy harvest. This article proposes novel machine learning and dynamic optimization-based approaches to handle the two challenges. Specifically, we first develop a novel set of features and use it in a low-power neural network architecture to predict future energy availability and uncertainty. The energy predictions and uncertainty are used in a dynamic optimization algorithm to optimally allocate the harvested energy. Experiments on solar energy data over 5 years from Golden, Colorado, show that the proposed energy prediction model achieves 3.4 J mean absolute error while having a coverage of 80%. Moreover, our energy management algorithm provides energy allocations that are within 2.5 J of an optimal Oracle with 2.65 mJ to 36.54 mJ of energy overhead.

Wearable devices that integrate multiple sensors, processors, and communication technologies have the potential to transform mobile health for remote monitoring of health parameters. However, the small form factor of the wearable devices limits the battery size and operating lifetime. As a result, the devices require frequent recharging, which has limited their widespread adoption. Energy harvesting has emerged as an effective method towards sustainable operation of wearable devices. Unfortunately, energy harvesting alone is not sufficient to fulfill the energy requirements of wearable devices. This paper studies the novel problem of adaptive energy management towards the goal of self-sustainable wearables by using harvested energy to supplement the battery energy and to reduce manual recharging by users. To solve this problem, we propose a principled algorithm referred as AdaEM. There are two key ideas behind AdaEM. First, it uses machine learning (ML) methods to learn predictive models of user activity and energy usage patterns. These models allow us to estimate the potential of energy harvesting in a day as a function of the user activities. Second, it reasons about the uncertainty in predictions and estimations from the ML models to optimize the energy management decisions using a dynamic robust optimization (DyRO) formulation. We propose a light-weight solution for DyRO to meet the practical needs of deployment. We validate the AdaEM approach on a wearable device prototype consisting of solar and motion energy harvesting using real-world data of user activities. Experiments show that AdaEM achieves solutions that are within 5% of the optimal with less than 0.005% execution time and energy overhead.

Human activity recognition (HAR) is widely used in applications ranging from activity tracking to rehabilitation of patients. HAR classifiers are typically trained with data collected from a known set of users while assuming that all the sensors needed for activity recognition are working perfectly and there are no missing samples. However, real-world usage of the HAR classifier may encounter missing data samples due to user error, device error, or battery limitations. The missing samples, in turn, lead to a significant reduction in accuracy. To address this limitation, we propose an adaptive method that either uses low-power mean imputation or generative adversarial imputation networks (GAIN) to recover the missing data samples before classifying the activities. Experiments on a public HAR dataset with 22 users show that the proposed robust HAR classifier achieves 94% classification accuracy with as much as 20% missing samples from the sensors with 390 μJ energy consumption per imputation.

Wearable devices are becoming increasingly popular for health and activity monitoring applications. These devices typically include small rechargeable batteries to improve user comfort. However, the small battery capacity leads to limited operating life, requiring frequent recharging. Recent research has proposed energy harvesting using light and user motion to improve the lifetime of wearable devices. Most energy harvesting approaches assume that the placement of the energy harvesting device and sensors required for health monitoring are the same. However, this assumption does not hold for several real-world applications. For example, motion energy harvesting using piezoelectric sensors is limited to the knees and elbows, while a sensor for heart rate monitoring must be placed on the chest for optimal performance. To address this challenge, we propose a novel dynamic energy management approach referred to as DIET for wearable health applications enabled by multiple sensors and energy harvesting devices. The key idea behind DIET is to harvest energy from multiple sources and optimally allocate it to each sensor using a lightweight optimization algorithm such that the overall utility for applications is maximized. Experiments on real-world data from four users over 30 days show that the DIET approach achieves utility within 10% of an offline Oracle.

Wearable devices are becoming popular for health and activity monitoring. The machine learning (ML) models for these applications are trained by collecting data in a laboratory with precise control of experimental settings. However, during real-world deployment/usage, the experimental settings (e.g., sensor position or sampling rate) may deviate from those used during training. This discrepancy can degrade the accuracy and effectiveness of the health monitoring applications. Therefore, there is a great need to develop reliable ML approaches that provide high accuracy for real-world deployment. In this paper, we propose a novel statistical optimization approach referred as StatOpt that automatically accounts for the real-world disturbances in sensing data to improve the reliability of ML models for wearable devices. We theoretically derive the upper bounds on sensor data disturbance for StatOpt to produce a ML model with reliability certificates. We validate StatOpt on two publicly available datasets for human activity recognition. Our results show that compared to standard ML algorithms, the reliable ML classifiers enabled by the StatOpt approach improve the accuracy up to 50% in real-world settings with zero overhead, while baseline approaches incur significant overhead and fail to achieve comparable accuracy.

Energy harvesting offers an attractive and promising mechanism to power low-energy devices. However, it alone is insufficient to enable an energy-neutral operation, which can eliminate tedious battery charging and replacement requirements. Achieving an energy-neutral operation is challenging since the uncertainties in harvested energy undermine the quality of service requirements. To address this challenge, we present a runtime energy-allocation framework that optimizes the utility of the target device under energy constraints using a rollout algorithm, which is a sequential approach to solve dynamic optimization problems. The proposed framework uses an efficient iterative algorithm to compute initial energy allocations at the beginning of a day. The initial allocations are then corrected at every interval to compensate for the deviations from the expected energy harvesting pattern. We evaluate this framework using solar and motion energy harvesting modalities andAmerican Time Use Surveydata from 4772 different users. Compared to prior techniques, the proposed framework achieves up to 35% higher utility even under energy-limited scenarios. Moreover, measurements on a wearable device prototype show that the proposed framework has$1000\times $smaller energy overhead than iterative approaches with a negligible loss in utility.

Internet of Things (IoT) devices are becoming popular in a number of transformative applications, including smart health, digital agriculture, and wide area sensing. However, small battery capacities and the need for frequent battery replacements or recharging have hindered their widespread adoption. Energy harvesting (EH) and management present a promising opportunity to enable long-term recharge free operation of IoT devices. State-of-the-art energy management approaches employ dynamic optimization methods to manage the harvested energy. However, the dynamic optimization methods are typically computationally intensive and lead to significant energy overhead for energy-constrained IoT devices. In contrast, this article proposes an imitation learning (IL)-based energy management algorithm that provides the energy budget or allocation for each decision interval without the need for dynamic optimization. We present an efficient approach to design Oracle policies that optimize the energy allocation of the IoT device to enable self-powered operation while maximizing the utility to the application. Then, we leverage the Oracle policy to train an online policy that performs near-optimal energy allocation at runtime. Our experiments with solar EH data for six years from three locations show that the proposed IL policies achieve allocation that is, on average, within 2.5 J of the Oracle, while having an energy consumption overhead of 154$\mu \text{J}$.

Movement disorders, such as Parkinson’s disease, affect more than 10 million people worldwide. Gait analysis is a critical step in the diagnosis and rehabilitation of these disorders. Specifically, step and stride lengths provide valuable insights into the gait quality and rehabilitation process. However, traditional approaches for estimating step length are not suitable for continuous daily monitoring since they rely on special mats and clinical environments. To address this limitation, this article presents a novel and practical step-length estimation technique using low-power wearable bend and inertial sensors. Experimental results show that the proposed model estimates step length with 5.49% mean absolute percentage error and provides accurate real-time feedback to the user.

Mobile system-on-chips (SoCs) are growing in their complexity and heterogeneity (e.g., Arm’s Big-Little architecture) to meet the needs of emerging applications, including games and artificial intelligence. This makes it very challenging to optimally manage the resources (e.g., controlling the number and frequency of different types of cores) at runtime to meet the desired trade-offs among multiple objectives such as performance and energy. This paper proposes a novel information-theoretic framework referred to as PaRMIS to create Pareto-optimal resource management policies for given target applications and design objectives. PaRMIS specifies parametric policies to manage resources and learns statistical models from candidate policy evaluation data in the form of target design objective values. The key idea is to select a candidate policy for evaluation in each iteration guided by statistical models that maximize the information gain about the true Pareto front. Experiments on a commercial heterogeneous SoC show that PaRMIS achieves better Pareto fronts and is easily usable to optimize complex objectives (e.g., performance per Watt) when compared to prior methods

Low-power internet of things devices have the potential to transform multiple fields including healthcare, environmental monitoring, and digital agriculture. However, the operating life of these devices is severely constrained by their small batteries that require frequent recharging. Harvesting energy from ambient sources has emerged as an effective approach to prolong the lifetime of these devices. The harvested energy must be carefully managed to ensure that sufficient energy is available when ambient energy is scarce. Prediction of the energy available in the future can aid energy management algorithms in making better decisions about the allocation of the available energy. This paper proposes a novel hierarchical machine learning model that considers recent history and daily variations to make accurate predictions of future energy availability. We also propose using online learning to adapt the model to seasonal and spatial variations in the harvested energy. Using real-world solar energy data from the National Renewable Energy Laboratory we show that the proposed approach has mean absolute errors of 1.5 J and 1.1 J in predictions under same and different locations, respectively. We also demonstrate that using the proposed approach in an energy management algorithm leads to 54% higher utility and 95% lower battery violations than the baseline.

Recent developments in stretchable and flexible sensing and processing technologies enable a wide range of wearable devices. These devices can pave the way to medical applications ranging from health and activity monitoring to diagnosis and treatments of movement disorders. However, recent studies show that this potential is hindered by both adaptation challenges that affect the end users and technology challenges faced by developers. This paper first summarizes the challenges faced by wearable devices targeting health and user activity monitoring applications. Then, it reviews recent research progress towards addressing these challenges in energy harvesting, energy management, flexible system design, and test areas.

Mobile platforms must satisfy the contradictory requirements of fast response time and minimum energy consumption as a function of dynamically changing applications. To address this need, systems-on-chip (SoC) that are at the heart of these devices provide a variety of control knobs, such as the number of active cores and their voltage/frequency levels. Controlling these knobs optimally at runtime is challenging for two reasons. First, the large configuration space prohibits exhaustive solutions. Second, control policies designed offline are at best sub-optimal, since many potential new applications are unknown at design-time. We address these challenges by proposing an online imitation learning approach. Our key idea is to construct an offline policy and adapt it online to new applications to optimize a given metric (e.g., energy). The proposed methodology leverages the supervision enabled by power-performance models learned at runtime. We demonstrate its effectiveness on a commercial mobile platform with 16 diverse benchmarks. Our approach successfully adapts the control policy to an unknown application after executing less than 25% of its instructions.

The use of wearable and mobile devices for health and activity monitoring is growing rapidly. These devices need to maximize their accuracy and active time under a tight energy budget imposed by battery and form-factor constraints. This paper considers energy harvesting devices that run on a limited energy budget to recognize user activities over a given period. We propose a technique to co-optimize the accuracy and active time by utilizing multiple design points with different energy-accuracy trade-offs. The proposed technique switches between these design points at runtime to maximize a generalized objective function under tight harvested energy budget constraints. We evaluate our approach experimentally using a custom hardware prototype and 14 user studies. It achieves 46% higher expected accuracy and 66% longer active time compared to the highest performance design point.

State-of-the-art mobile processors can deliver fast response time and high throughput to maximize the user experience. However, high performance comes at the expense of larger power density, which leads to higher skin temperatures. Since this can degrade the user experience, there is a strong need for power consumption and thermal analysis in mobile processors. In this paper, we first perform experiments on the Nexus 6P phone to study the power, performance and thermal behavior of modern smartphones. Using the insight from these experiments, we propose a control algorithm that throttles select applications without affecting other apps. We demonstrate our governor on the Exynos 5422 processor employed in the Odroid-XU3 board.

Flexible hybrid electronics (FHE) is emerging as a promising solution to combine the benefits of printed electronics and silicon technology. FHE has many high-impact potential areas, such as wearable applications, health monitoring, and soft robotics, due to its physical advantages, which include light weight, low cost and the ability conform to different shapes. However, physical deformations in the field can lead to significant testing and validation challenges. For example, designers must ensure that FHE devices continue to meet their specs even when the components experience stress due to bending. Hence, physical deformation, which is hard to emulate, has to be part of the test procedures for FHE devices. This paper is the first to analyze stress experience at different parts of FHE devices under different bending conditions. We develop a novel methodology to maximize the test coverage with minimum number of text vectors with the help of a mixed integer linear programming formulation. We validate the proposed approach using an FHE prototype and COMSOL Multiphysics simulations.

Human activity recognition (HAR) has recently received significant attention due to its wide range of applications in health and activity monitoring. The nature of these applications requires mobile or wearable devices with limited battery capacity. User surveys show that charging requirement is one of the leading reasons for abandoning these devices. Hence, practical solutions must offer ultra-low power capabilities that enable operation on harvested energy. To address this need, we present the first fully integrated custom hardware accelerator (HAR engine) that consumes 22.4 μJ per operation using a commercial 65 nm technology. We present a complete solution that integrates all steps of HAR , i.e., reading the raw sensor data, generating features, and activity classification using a deep neural network (DNN). It achieves 95% accuracy in recognizing 8 common human activities while providing three orders of magnitude higher energy efficiency compared to existing solutions.

The complexity of heterogeneous mobile platforms is growing at a rate faster than our ability to manage them optimally at runtime. For example, state-of-the-art systems-on-chip (SoCs) enable controlling the type (Big/Little), number, and frequency of active cores. Managing these platforms becomes challenging with the increase in the type, number, and supported frequency levels of the cores. However, existing solutions used in mobile platforms still rely on simple heuristics based on the utilization of cores. This paper presents a novel and practical imitation learning (IL) framework for dynamically controlling the type (Big/Little), number, and the frequencies of active cores in heterogeneous mobile processors. We present efficient approaches for constructing an Oracle policy to optimize different objective functions, such as energy and performance per Watt (PPW). The Oracle policies enable us to design low-overhead power management policies that achieve near-optimal performance matching the Oracle. Experiments on a commercial platform with 19 benchmarks show on an average 101% PPW improvement compared to the default interactive governor.

Human activity recognition (HAR) has attracted significant research interest due to its applications in health monitoring and patient rehabilitation. Recent research on HAR focuses on using smartphones due to their widespread use. However, this leads to inconvenient use, limited choice of sensors and inefficient use of resources, since smartphones are not designed for HAR. This paper presents the first HAR framework that can perform both online training and inference. The proposed framework starts with a novel technique that generates features using the fast Fourier and discrete wavelet transforms of a textile-based stretch sensor and accelerometer data. Using these features, we design a neural network classifier which is trained online using the policy gradient algorithm. Experiments on a low power IoT device (T1-CC2650 MCU) with nine users show 97.7% accuracy in identifying six activities and their transitions with less than 12.5 mW power consumption.

Energy efficiency and performance of heterogeneous multiprocessor systems-on-chip (SoC) depend critically on utilizing a diverse set of processing elements and managing their power states dynamically. Dynamic resource management techniques typically rely on power consumption and performance models to assess the impact of dynamic decisions. Despite the importance of these decisions, many existing approaches rely on fixed power and performance models learned offline. This paper presents an online learning framework to construct adaptive analytical models. We illustrate this framework for modeling GPU frame processing time, GPU power consumption and SoC power-temperature dynamics. Experiments on Intel Atom E3826, Qualcomm Snapdragon 810, and Samsung Exynos 5422 SoCs demonstrate that the proposed approach achieves less than 6% error under dynamically varying workloads.

With increasing diversity of supply chains from design to delivery, there is an increasing risk that unauthorized changes can be made within an IC. One of the motivations for this type of change is to learn important information (such as encryption keys, spreading codes) from the hardware, and transmit this information to a malicious party. To evade detection, such unauthorized communication can be hidden within legitimate bursts of transmit signal. In this article, we present several signal processing techniques to detect unauthorized transmissions which can be hidden within the legitimate signal. We employ a scheme where the legitimate transmission is configured to emit a single sinusoidal waveform. We use time and spectral domain analysis techniques to explore the transmit spectrum. Since every transmission, no matter how low the signal power is, must have a spectral signature, we identify unauthorized transmission by eliminating the desired signal from the spectrum after capture. Experiment results show that when spread spectrum techniques are used, the presence of an unauthorized signal can be determined without the need for decoding the malicious signal. The proposed detection techniques need to be used as enhancements to the regular testing and verification procedures if hardware security is a concern.

State-of-the-art mobile platforms are powered by heterogeneous system-on-chips that integrate multiple CPU cores, a GPU, and many specialized processors. Competitive performance on these platforms comes at the expense of increased power density due to their small form factor. Consequently, the skin temperature, which can degrade the experience, becomes a limiting factor. Since using a fan is not a viable solution for hand-held devices, there is a strong need for dynamic thermal and power management (DTPM) algorithms that can regulate temperature with minimal performance impact. This paper presents a DTPM algorithm, which uses a practical temperature prediction methodology based on system identification. The proposed algorithm dynamically computes a power budget using the predicted temperature. This budget is used to throttle the frequency and number of cores to avoid temperature violations with minimal impact on the system performance. Our experimental measurements on two different octa-core big.LITTLE processors and common Android applications demonstrate that the proposed technique predicts the temperature with less than 5% error across all benchmarks. Using this prediction, the proposed DTPM algorithm successfully regulates the maximum temperature and decreases the temperature violations by one order of magnitude while also reducing the total power consumption on average by 7% compared with the default solution.

Wearable internet of things (IoT) devices are becoming popular due to their small form factor and low cost. Potential applications include human health and activity monitoring by embedding sensors such as accelerometer, gyroscope, and heart rate sensor. However, these devices have severely limited battery capacity, which requires frequent recharging. Harvesting ambient energy and optimal energy allocation can make wearable IoT devices practical by eliminating the charging requirement. This paper presents a near-optimal runtime energy management technique by considering the harvested energy. The proposed solution maximizes the performance of the wearable device under minimum energy constraints. We show that the results of the proposed algorithm are, on average, within 3% of the optimal solution computed offline.

Modern multiprocessor system-on-chips (SoCs) integrate multiple heterogeneous cores to achieve high energy efficiency. The power consumption of each core contributes to an increase in the temperature across the chip floorplan. In turn, higher temperature increases the leakage power exponentially, and leads to a positive feedback with nonlinear dynamics. This paper presents a power-temperature stability and safety analysis technique for multiprocessor systems. This analysis reveals the conditions under which the power-temperature trajectory converges to a stable fixed point. We also present a simple formula to compute the stable fixed point and maximum thermally-safe power consumption at runtime . Hardware measurements on a state-of-the-art mobile processor show that our analytical formulation can predict the stable fixed point with an average error of 2.6%. Hence, our approach can be used at runtime to ensure thermally safe operation and guard against thermal threats.

Modern multiprocessor systems-on-chip (MpSoCs) offer tremendous power and performance optimization opportunities by tuning thousands of potential voltage, frequency and core configurations. As the workload phases change at runtime, different configurations may become optimal with respect to power, performance or other metrics. Identifying the optimal configuration at runtime is infeasible due to the large number of workloads and configurations. This paper proposes a novel methodology that can find the Pareto-optimal configurations at runtime as a function of the workload. To achieve this, we perform an extensive offline characterization to find classifiers that map performance counters to optimal configurations. Then, we use these classifiers and performance counters at runtime to choose Pareto-optimal configurations. We evaluate the proposed methodology by maximizing the performance per watt for 18 single- and multi-threaded applications. Our experiments demonstrate an average increase of 93%, 81% and 6% in performance per watt compared to the interactive, ondemand and powersave governors, respectively.

Flexible systems that can conform to any shape are desirable for wearable applications. Over the past decade, there have been tremendous advances in the domain of flexible electronics which enabled printing of devices, such as sensors on a flexible substrate. Despite these advances, pure flexible electronics systems are limited by poor performance and large feature sizes. Flexible hybrid electronics (FHE) is an emerging technology which addresses these issues by integrating high performance rigid integrated circuits and flexible devices. Yet, there are no system-level design flows and algorithms for the design of FHE systems. To this end, this paper presents a multi-objective design algorithm to implement a target application optimally using a library of rigid and flexible components. Our algorithm produces a set of Pareto frontiers that optimize the physical flexibility, energy per operation and area metrics. Simulation studies show a 32× range in area and 4× range in flexibility across the set of Pareto-optimal design points.