Trust in digital systems depends on secure hardware, often assured through HRE. This work develops methods for investigating human problem-solving processes in HRE, an underexplored yet critical aspect. Since reverse engineers rely heavily on visual information, eye tracking holds promise for studying their cognitive processes. To gain further insights, we additionally employ verbal thought protocols during and immediately after HRE tasks: Concurrent and Retrospective Think Aloud. We evaluate the combination of eye tracking and Think Aloud with 41 participants in an HRE simulation. Eye tracking accurately identifies fixations on individual circuit elements and highlights critical components. Based on two use cases, we demonstrate that eye tracking and Think Aloud can complement each other to improve data quality. Our methodological insights can inform future studies in HRE, a specific setting of human-computer interaction, and in other problem-solving settings involving misleading or missing information.

Federated learning (FL) has been increasingly widely used for distributed and privacy-preserving machine learning (ML) environments, as the raw training data can stay local to clients while leveraging model updates from individual clients. Homomorphic encryption (HE) technologies can provide additional privacy protection for FL by encrypting the model update parameters while allowing model aggregation on a remote server. Although HE-enabled FL seems to be a promising privacy-preserving ML solution, it requires significantly more computational and memory resources, requiring a dedicated hardware and software platform. In this paper, we discuss preliminary but concrete and realizable research ideas for analyzing the requirements for HE-enabled FL and for designing a hardware and software platform. Furthermore, we propose a platform co-design process that considers various design stages and challenges in the platform co-design.

Precision livestock farming (PLF) has been transformed by machine learning (ML), enabling more precise and timely interventions that enhance overall farm productivity, animal welfare, and environmental sustainability. However, despite the availability of various sensing technologies, few datasets leverage multiple modalities, which are crucial for developing more accurate and efficient monitoring devices and ML models. To address this gap, we present MmCows, a multimodal dataset for dairy cattle monitoring. This dataset comprises a large amount of synchronized, high-quality measurement data on behavioral, physiological, and environmental factors. It includes two weeks of data collected using wearable and implantable sensors deployed on ten milking Holstein cows, such as ultra-wideband (UWB) sensors, inertial sensors, and body temperature sensors. In addition, it features 4.8 million frames of high-resolution image sequences from four isometric view cameras, as well as temperature and humidity data from environmental sensors. We also gathered milk yield data and outdoor weather conditions. One full day’s worth of image data is annotated as ground truth, totaling 20,000 frames with 213,000 bounding boxes of 16 cows, along with their 3D locations and behavior labels. An extensive analysis of MmCows is provided to evaluate the modalities individually and their complementary benefits. The release of MmCows and its benchmarks will facilitate research on multimodal monitoring of dairy cattle, thereby promoting sustainable dairy farming. The dataset and the code for benchmarks are available at https://github.com/neis-lab/mmcows.

Binary neural networks (BNNs) substitute complex arithmetic operations with simple bit-wise operations. The binarized weights and activations in BNNs can drastically reduce memory requirement and energy consumption, making it attractive for edge ML applications with limited resources. However, the severe memory capacity and energy constraints of low-power edge devices call for further reduction of BNN models beyond binarization. Weight pruning is a proven solution for reducing the size of many neural network (NN) models, but the binary nature of BNN weights make it difficult to identify insignificant weights to remove.

Cattle diseases have a significant negative impact not only on the animals' welfare but also on the economic performance of the cattle industry [1], [2]. For example, Bovine Respiratory Disease is responsible for approximately 75% of the morbidity and 57% of the mortality in US feedlots, which is estimated to cost the agriculture industry about $1B annually [1], [2]. The current management practice to diagnose and select cattle for treatment is a widespread clinical scoring system called DART (Depression, Appetite, Respiration, and Temperature). DART requires manual labor and skilled personnel, which is a limiting factor due to labor-shortage in several industry sectors, including agriculture [3]. Therefore, a continuous and automated IoT solution to predict the health state of a cow is a critical tool for the cattle industry.

Heat stress, caused by a warming climate and the increasingly high milk-producing dairy cattle, is one of the major threats to the well-being of dairy cattle as well as the economic, environmental, and social sustainability of dairy farming around the world. Timely identification of cows under heat stress is crucial to improving animal welfare, preventing milk production losses, and preserving water and energy for cooling.

Smart home technologies offer many benefits to users. Yet, they also carry complex security and privacy implications that users often struggle to assess and account for during adoption. To better understand users’ considerations and attitudes regarding smart home security and privacy, in particular how users develop them progressively, we conducted a qualitative content analysis of 4,957 Reddit comments in 180 security- and privacy-related discussion threads from /r/homeautomation, a major Reddit smart home forum. Our analysis reveals that users’ security and privacy attitudes, manifested in the levels of concern and degree to which they incorporate protective strategies, are shaped by multi-dimensional considerations. Users’ attitudes evolve according to changing contextual factors, such as adoption phases, and how they become aware of these factors. Further, we describe how online discourse about security and privacy risks and protections contributes to individual and collective attitude development. Based on our findings, we provide recommendations to improve smart home designs, support users’ attitude development, facilitate information exchange, and guide future research regarding smart home security and privacy.

Live-streaming video requires a lot of CPU-intensive transcoding so that viewers can receive video at bitrates appropriate to their devices and network conditions, which is necessary for a good quality of experience (QoE). We allocate transcoding tasks to edge-servers in a multiple-access edge-computing (MEC) architecture, taking into account server capacity, wireless network coverage, and the cost budget of broadcasters, as well as QoE. Our algorithm first chooses candidate transcoding tasks by giving higher priority to the tasks that make the most cost-effective contribution to popularity-weighted video quality (PWQ). It assigns these tasks to edge-servers in a greedy manner, taking network coverage and computational load into account. Subsequently, it meets a cost budget by reassigning some tasks and removing other assignments altogether, while trying to minimize the effect of these alterations on total PWQ. Simulation results show that our scheme achieves 0.06% to 94.62% (average 25.3%) more PWQ than alternative schemes under the same cost budget.

In combinational logic neural networks (CLNNs), neurons are realized as combinational logic circuits or look-up tables (LUTs). They make make extremely low-latency inference possible by performing the computation with pure hardware without loading weights from the memory. The high throughput, however, is powered by massively parallel logic circuits or LUTs and hence comes with high area occupancy and high energy consumption. We present SYNTHNET, a novel CLNN design method that effectively identifies and keeps only the sublogics that play a critical role in the accuracy and remove those which do not contribute to improving the accuracy. It captures the abundant redundancy in NNs that can be exploited only in CLNNs, and thereby dramatically reduces the energy consumption of CLNNs with minimal accuracy degradation. We prove the efficacy of SYNTHNET on the CIFAR-10 dataset, maintaining a competitive accuracy while successfully replacing layers of a VGG-style network which traditionally uses memory-based floating point operations with combinational logic. Experimental results suggest our design can reduce energy-consumption of CLNNs more than 90% compared to the state-of-the-art design.

Brain computer interfaces (BCIs) have been widely adopted to enhance human perception via brain signals with abundant spatial-temporal dynamics, such as electroencephalogram (EEG). In recent years, BCI algorithms are moving from classical feature engineering to emerging deep neural networks (DNNs), allowing to identify the spatial-temporal dynamics with improved accuracy. However, existing BCI architectures are not leveraging such dynamics for hardware efficiency. In this work, we present uBrain, a unary computing BCI architecture for DNN models with cascaded convolutional and recurrent neural networks to achieve high task capability and hardware efficiency. uBrain co-designs the algorithm and hardware: the DNN architecture and the hardware architecture are optimized with customized unary operations and immediate signal processing after sensing, respectively. Experiments show that uBrain, with negligible accuracy loss, surpasses the CPU, systolic array and stochastic computing baselines in on-chip power efficiency by 9.0×, 6.2× and 2.0×.

Artificial intelligence (AI) based wearable applications collect and process a significant amount of streaming sensor data. Transmitting the raw data to cloud processors wastes scarce energy and threatens user privacy. Wearable edge AI devices should ideally balance two competing requirements: (1) maximizing the energy efficiency using targeted hardware accelerators and (2) providing versatility using general-purpose cores to support arbitrary applications. To this end, we present an open-source domain-specific programmable system-on-chip (SoC) that combines a RISC-V core with a meticulously determined set of accelerators targeting wearable applications. We apply the proposed design method to design an FPGA prototype and six real-life use cases to demonstrate the efficacy of the proposed SoC. Thorough experimental evaluations show that the proposed SoC provides up to 9.1 × faster execution and up to 8.9 × higher energy efficiency than software implementations in FPGA while maintaining programmability.

For many applications that exhibit strong error resilience, such as machine learning and signal processing, energy efficiency and performance can be dramatically improved by allowing for slight errors in intermediate computations. Iterative methods (IMs), wherein the solution is improved over multiple executions of an approximation algorithm, allow for energy-quality trade-off at run-time by adjusting the number of iterations (NOI). However, in prior IM circuits, NOI adjustment has been made based on a pre-characterized NOI-quality mapping, which is input-agnostic thus results in an undesirable large variation in output quality. In this paper, we propose a novel design framework that incorporates a lightweight quality controller that makes input-dependent predictions on the output quality and determines the optimal NOI at run-time. The proposed quality controller is composed of accurate yet low-overhead NOI predictors, generated by a novel logic reduction technique. We evaluate the proposed design framework on several IM circuits and demonstrate significant improvements in energy-quality performance.

Context-based authentication is a method for transparently validating another device's legitimacy to join a network based on location. Devices can pair with one another by continuously harvesting environmental noise to generate a random key with no user involvement. However, there are gaps in our understanding of the theoretical limitations of environmental noise harvesting, making it difficult for researchers to build efficient algorithms for sampling environmental noise and distilling keys from that noise. This work explores the information-theoretic capacity of context-based authentication mechanisms to generate random bit strings from environmental noise sources with known properties. Using only mild assumptions about the source process's characteristics, we demonstrate that commonly-used bit extraction algorithms extract only about 10% of the available randomness from a source noise process. We present an efficient algorithm to improve the quality of keys generated by context-based methods and evaluate it on real key extraction hardware. MOONSHINE is a randomness distiller which is more efficient at extracting bits from an environmental entropy source than existing methods. Our techniques nearly double the quality of keys as measured by the NIST test suite, producing keys that can be used in real-world authentication scenarios.

Iterative computing, where the output accuracy gradually improves over multiple iterations, enables dynamic reconfiguration of energy-quality trade-offs by adjusting the latency (i.e., number of iterations). In order to take full advantage of the dynamic reconfigurability of iterative computing hardware, an efficient method for determining the optimal latency is crucial. In this paper, we introduce an integer linear programming (ILP)- based scheduling method to determine the optimal latency of iterative computing hardware. We consider the input-dependence of output accuracy of approximate hardware using data-driven error modeling for accurate quality estimation. The proposed method finds optimal or near-optimal latency with a significant speedup compared to exhaustive search and decision tree-based optimization.

Linear multiply-accumulate (MAC) operations have been the main focus of prior efforts in improving the energy efficiency of neural network inference due to their dominant contribution to energy consumption in traditional models. On the other hand, nonlinear operations, such as division, exponentiation, and logarithm, that are becoming increasingly significant in emerging neural network models, have been largely underexplored. In this paper, we propose UNO, a low-area, low-energy processing element that virtualizes the Taylor approximation of nonlinear operations on top of off-the-shelf linear MAC units already present in inference hardware. Such virtualization approximates multiple nonlinear operations in a unified, MAC-compatible manner to achieve dynamic run-time accuracy-energy scaling. Compared to the baseline, our scheme reduces the energy consumption by up to 38.4% for individual operations and increases the energy efficiency by up to 274.5% for emerging neural network models with negligible inference loss

General matrix multiplication (GEMM) is universal in various applications, such as signal processing, machine learning, and computer vision. Conventional GEMM hardware architectures based on binary computing exhibit low area and energy efficiency as they scale due to the spatial nature of number representation and computing. Unary computing, on the other hand, can be performed with extremely simple processing units, often just with a single logic gate. But currently there exist no efficient architectures for unary GEMM. In this paper, we present uGEMM, an area- and energy-efficient unary GEMM architecture enabled by novel arithmetic units. The proposed design relaxes previously-imposed constraints on input bit streams-low correlation and long stream length- and achieves superior area and energy efficiency over existing unary systems. Furthermore, uGEMM's output bit streams exhibit higher accuracy and faster convergence, enabling dynamic energy-accuracy scaling on resource-constrained systems.

The emergence of advanced in-vehicle infotainment (IVI) systems, such as Apple CarPlay and Android Auto, calls for fast and intuitive device pairing mechanisms to discover newly introduced devices and make or break a secure, high-bandwidth wireless connection. Current pairing schemes are tedious and lengthy as they typically require users to go through pairing and verification procedures by manually entering a predetermined or randomly generated pin on both devices. This inconvenience usually results in prolonged usage of old pins, significantly degrading the security of network connections.

NAND flash storage has become a standard choice in consumer electronics and is gaining popularity in enterprise systems due to its superior performance and low-power consumption. While its cost disadvantage is rapidly fading thanks to multibit cell technologies and 3-D stacking architectures, the challenge of limited endurance is still lingering and is expected to become more daunting as bits-per-cell continues to increase. In this article, we propose a novel flash translation layer (FTL) design named AxFTL (Approximate FTL) that extends the lifetime of NAND flash storage for error-tolerant applications. For error-tolerant data, AxFTL adopts shallow erase that lowers erase voltage to reduce the erase-induced wearing at the cost of an increased error rate. AxFTL manages multiple groups of blocks by error rates and allocates them according to the error tolerance of write requests. The key components of AxFTL include error tolerance-aware garbage collection and wear leveling schemes that manage the blocks with different error rates with minimal overhead. We implement AxFTL in an SSD simulator for the evaluation of the lifetime improvement and the actual allocation of the blocks. For application-level evaluation, we apply AxFTL to compressed video storage and evaluate the quality of video playback. Our experimental results show that AxFTL greatly improves the lifetime of NAND flash storage by 61% while maintaining a high structural similarity (SSIM) of 0.86 as compared to the conventional FTL.

Approximate computing can significantly improve the energy efficiency of arithmetic operations in error-resilient applications. In this paper, we propose an approximate divider design that facilitates dynamic energy-quality scaling. Conventional approximate dividers lack runtime energy-quality scalability, which is the key to maximizing the energy efficiency while meeting dynamically varying accuracy requirements. Our divider design, named SAADI, makes an approximation to the reciprocal of the divisor in an incremental manner, thus the division speed and energy efficiency can be dynamically traded for accuracy by controlling the number of iterations. For the approximate 8-bit division of 32-bit/16-bit division, the average accuracy of SAADI can be adjusted in between 92.5% and 99.0% by varying latency up to 7x. We evaluate the accuracy and energy consumption of SAADI for various design parameters and demonstrate its efficacy for low-power signal processing applications.

Biometrics have been widely adopted for enhancing user authentication, benefiting usability by exploiting pervasive and collectible unique characteristics from physiological or behavioral traits of human. However, successful attacks on "static" biometrics such as fingerprints have been reported where an adversary acquires users' biometrics stealthily and compromises non-resilient biometrics.

From signal processing to emerging deep neural networks, a range of applications exhibit intrinsic error resilience. For such applications, approximate computing opens up new possibilities for energy-efficient computing by producing slightly inaccurate results using greatly simplified hardware. Adopting this approach, a variety of basic arithmetic units, such as adders and multipliers, have been effectively redesigned to generate approximate results for many error-resilient applications.In this work, we propose SECO, an approximate exponential function unit (EFU). Exponentiation is a key operation in many signal processing applications and more importantly in spiking neuron models, but its energy-efficient implementation has been inadequately explored. We also introduce a cross-layer design method for SECO to optimize the energy-accuracy trade-off. At the algorithm level, SECO offers runtime scaling between energy efficiency and accuracy based on approximate Taylor expansion, where the error is minimized by optimizing parameters using discrete gradient descent at design time. At the circuit level, our error analysis method efficiently explores the design space to select the energy-accuracy-optimal approximate multiplier at design time. In tandem, the cross-layer design and runtime optimization method are able to generate energy-efficient and accurate approximate EFU designs that are up to 99.7% accurate at a power consumption of 3.73 pJ per exponential operation. SECO is also evaluated on the adaptive exponential integrate-and-fire neuron model, yielding only 0.002% timing error and 0.067% value error compared to the precise neuron model.

Smartphones are the most commonly used computing platform for accessing sensitive and important information placed on the Internet. Authenticating the smartphone's identity in addition to the user's identity is a widely adopted security augmentation method since conventional user authentication methods, such as password entry, often fail to provide strong protection by itself.

Advancement in energy storage technologies has enabled the emergence of new applications, such as grid-scale and domestic energy storage and electric vehicles (EVs). Despite the advances, the loss of energy during operation is one of the major challenges in improving the energy efficiency and the lifetime of storage systems. Reconfigurable energy storage allows the dynamic rearrangement of the unit cells to reduce voltage variations and therefore increase energy efficiency, but the introduction of switching circuit increases the upfront cost of the systems. Therefore, determining the granularity of reconfiguration that achieves high energy efficiency at a reasonable cost is a crucial design decision. However, the design space exploration for finding the optimal granularity often involves time-consuming evaluation of numerous feasible solutions. In this paper, we propose a novel algorithm to accelerate the design space exploration for reconfigurable energy storage in a branch-and-bound manner. The proposed algorithm finds a set of Pareto front solutions in terms of cost and energy efficiency with a reduced amount of computation. We apply the proposed algorithm to the design of supercapacitor energy storage for an EV, and the experimental result shows a reduction in computation by 45.4% on average with only 6.1% of non-Pareto (but still near-optimal) solutions included in the suggested solutions.

Energy efficiency is one of the most crucial constraints that dictate performance, lifetime, form factor, and cost in modern computing system design. However, the energy efficiency improvement driven by semiconductor technology scaling is coming to an end with the prediction of the end of Moore's law in the near future. Approximate computing is a new paradigm to accomplish energy-efficient computing in this twilight of Moore's law by relaxing exactness requirement of computation results for intrinsically error-resilient applications, such as some machine learning and signal processing. In this paper, we propose an approximate binary divider design that features dynamic configurability of accuracy and energy consumption. Conventional approximate binary dividers lack runtime energy-quality scalability, which is the key to maximizing energy efficiency while meeting the dynamically varying accuracy requirements of the application. Our divider, named Scalable Accuracy Approximate Divider with Error Compensation (SAADI-EC), supports dynamic energy-quality scalability by the incremental approximation of the reciprocal of the divisor using Taylor series expansion. As a result, the speed and energy efficiency of division can be dynamically traded for accuracy by controlling the number of iterations for the approximation. In addition, SAADI-EC corrects the approximation error using simple, yet effective error compensation hardware to greatly improve the accuracy compared to the base implementation, SAADI. For the 8-bit approximation of 32-bit/16-bit division, the average accuracy of SAADI-EC can be adjusted from 94.2% to 99.6% by varying latency and energy 7×. In terms of energy × delay cost, our design costs up to 87% less than other approximate binary dividers for the same accuracy level. We evaluate the accuracy and energy consumption of SAADI-EC for various design parameters and demonstrate its efficacy for low-power signal processing applications including k-means color quantization, JPEG image compression, and image division for video sequences.

Physically unclonable functions (PUFs) have proved to be an effective measure for secure device authentication and key generation. We propose a novel PUF design, named CamPUF, based on commercial off-the-shelf CMOS image sensors, which are ubiquitously available in almost all mobile devices. The inherent process mismatch between pixel sensors and readout circuits in an image sensor manifests as unique fixed pattern noise (FPN) in the image. We exploit FPN caused by dark signal non-uniformity (DSNU) as the basis for implementing the PUF. DSNU can be extracted only from dark images that are not shared with others, and only the legitimate user can obtain it with full control of the image sensor. Compared to other FPN components that can be extracted from shared images, DSNU facilitates more secure and usable device authentication. We present an efficient and reliable key generation procedure for use in wireless low-power devices. We implement CamPUF on Google Nexus 5X and Nexus 5 and evaluate the uniqueness and robustness of the keys, as well as its security against counterfeiting. We demonstrate that it discriminates legitimate and illegitimate authentication attempts without confusion.

The emergence of the Internet of Things (IoT) and pervasive computing challenges in securely and conveniently connecting devices with limited user interfaces. In particular, discovering and bootstrapping a wireless connection (e.g., Wi-Fi and Bluetooth Low Energy) between two devices that share no prior knowledge, commonly known as pairing, often requires users to go through cumbersome tasks of manually discovering the target device and entering a long passkey. When the devices do not have a proper user interface to enter a passkey, the security of pairing is often given up, leaving the communication vulnerable to a number of attacks. To alleviate this challenge, we propose a usable and secure out-of-band (OOB) communication method called SyncVibe, leveraging the inherent nature of close-proximity transmission of mechanical vibration. SyncVibe utilizes a vibration motor and an accelerometer, that are already ubiquitously available or easy to embed in mobile and wearable devices, to transmit and receive pairing information. By simply keeping two devices in direct contact, the user can bootstrap a secure, high-bandwidth wireless connection without manual pairing procedures. The proposed method maximizes accuracy and effective data throughput with a vibration clock recovery technique, which inserts a minimal amount of extra bit patterns to assure synchronization between the transmitter and the receiver. In addition, SyncVibe can automatically adjust its detection thresholds in response to various vibration noises and transmission media. Our implementation of SyncVibe demonstrates high-accuracy transmission, proving itself as a suitable OOB communication channel for short data transmission for secure device pairing.

Mobile, wearable, and implantable devices integrate an increasing number and variety of sensors such as microphones, image sensors, and accelerometers. These devices spend substantial amounts of time reading the sensors within them, thereby incurring significant energy dissipation over off-chip serial interconnects. This paper proposes AXSERBUS, a quality-configurable approximate serial bus that exploits the locality of sensory data and the error resiliency of sensing applications to reduce energy dissipation. AXSERBUS significantly reduces signal transitions by encoding the differentials of sensory data in three encoding modes, depending on the magnitude of the differentials: very small differentials are zeroed out, incurring no energy dissipation; intermediate differentials are encoded using special low-transition count patterns; and for high differentials, the absolute value (not the differential) of the data is transmitted. Compared to previous schemes, the proposed multi-level encoding results in more data being encoded as low-energy patterns. In addition, in the intermediate differential encoding mode, the differentials are encoded in an approximate manner, and the approximation bounds are proportional to the magnitude of the differentials. Since small differentials are more frequent than large differentials in sensory data, the proposed encoding scheme also minimizes quality degradation. We demonstrate that AXSERBUS achieves improved energy vs. quality tradeoffs compared to previous schemes. In the context of an optical character recognition (OCR) application, AXSERBUS achieves 79.4% reduction in dynamic power dissipation, while maintaining accuracy above 95%.

It is projected that, within the coming decade, there will be more than 50 billion smart objects connected to the Internet of Things (IoT). These smart objects, which connect the physical world with the world of computing infrastructure, are expected to pervade all aspects of our daily lives and revolutionize a number of application domains such as healthcare, energy conservation, transportation, etc. In this paper, we present an overview of the challenges involved in designing energy-efficient IoT edge devices and describe recent research that has proposed promising solutions to address these challenges. First, we outline the challenges involved in efficiently supplying power to an IoT device. Next, we discuss the role of emerging memory technologies in making IoT devices energy-efficient. Finally, we discuss the potential impact that approximate computing can have in increasing the energy-efficiency of wearables and other compute-intensive IoT devices.

The lack of post-deployment visibility into system behavior is one of the major challenges in ensuring the reliable operation of implantable medical devices (IMDs). While wireless connectivity is becoming common in IMDs for monitoring device status, conventional wireless links incur significant energy overheads for data acquisition, processing, and active radio transmission. While low-power transceivers have been introduced to reduce the energy consumed by the radio itself, the energy consumed by the microcontroller for processing data and controlling the radio has often been overlooked. As a result, in IMDs that have a stringent energy constraint, prolonged signal monitoring over a wireless channel is infeasible due to this prohibitively high power consumption.

Implantable and wearable medical devices are used for monitoring, diagnosis, and treatment of an ever-increasing range of medical conditions, leading to an improved quality of life for patients. The addition of wireless connectivity to medical devices has enabled post-deployment tuning of therapy and access to device data virtually anytime and anywhere but, at the same time, has led to the emergence of security attacks as a critical concern. While cryptography and secure communication protocols may be used to address most known attacks, the lack of a viable secure connection establishment and key exchange mechanism is a fundamental challenge that needs to be addressed. We propose a vibration-based secure side channel between an external device (medical programmer or smartphone) and a medical device. Vibration is an intrinsically short-range, user-perceptible channel that is suitable for realizing physically secure communication at low energy and size/weight overheads. We identify and address key challenges associated with the vibration channel, and propose a vibration-based wakeup and key exchange scheme, named SecureVibe, that is resistant to battery drain attacks. We analyze the risk of acoustic eavesdropping attacks and propose an acoustic masking countermeasure. We demonstrate and evaluate vibration-based wakeup and key exchange between a smartphone and a prototype medical device in the context of a realistic human body model.

This paper pioneers the maximum power point tracking (MPPT) of photovoltaic (PV) cells that directly supply power to a microprocessor without an energy storage element (a battery or a large-size capacitor) nor power converters. The maximum power point tracking is conventionally performed by an MPPT charger that stores in the energy storage element, and a voltage regulator (typically a DC-DC converter) produces a proper voltage level for the microprocessor. The energy storage element is an energy buffer and makes it possible to perform MPPT of the PV cells and power management of the microprocessor independently. However, the energy storage element, MPPT charger and DC-DC converter cause seriously limited lifetime (when a typical battery is adopted), significant energy loss (typically over 20%), increased weight/volume and high cost, etc. The proposed method enables extremely fine-grain dynamic power management (DPM) in every a few hundred microseconds and performs the MPPT without using an MPPT charger and a DC-DC converter as well as an energy storage element. We achieve 84.5% of energy harvesting efficiency using the proposed setup with huge reduction in cost, weight and volume, and extended lifetime, which is not even numerically comparable with conventional MPPT methods.

Various industry forecasts project that, by 2020, there will be around 50 billion devices connected to the Internet of Things (IoT), helping to engineer new solutions to societal-scale problems such as healthcare, energy conservation, transportation, etc. Most of these devices will be wireless due to the expense, inconvenience, or in some cases, the sheer infeasibility of wiring them. Further, many of them will have stringent size constraints. With no cord for power and limited space for a battery, powering these devices (to achieve several months to possibly years of unattended operation) becomes a daunting challenge. This paper highlights some promising directions for addressing this challenge, focusing on three main building blocks: (a) the design of ultra-low power hardware platforms that integrate computing, sensing, storage, and wireless connectivity in a tiny form factor, (b) the development of intelligent system-level power management techniques, and (c) the use of environmental energy harvesting to make IoT devices self-powered, thus decreasing -- in some cases, even eliminating -- their dependence on batteries. We discuss these building blocks in detail and illustrate case-studies of systems that use them judiciously, including the QUBE wireless embedded platform, which exploits the characteristics of emerging non-volatile memory technologies to seamlessly and efficiently enable long-running computations in systems that experience frequent power loss (i.e., intermittently powered systems).

Partial shading is a serious obstacle to the effective utilization of photovoltaic (PV) systems since it can result in a significant degradation in the PV system output power. A PV system is organized as a series connection of PV modules, each module comprising a number of series-parallel connected PV cells. Backup PV cell employment and PV module reconfiguration techniques have been proposed to improve the performance of the PV system under the partial shading effects. However, these approaches are not very effective since they are costly in terms of their PV cell count and/or cell connectivity requirements. In contrast, this paper presents a cost-effective, reconfigurable PV module architecture with integrated switches in each PV cell. This paper also presents a dynamic programming algorithm to adaptively produce near-optimal reconfigurations of each PV module so as to maximize the PV system output power under any partial shading pattern. We implement a working prototype of reconfigurable PV module with 16 PV cells and confirm 45.2% output power level improvement. Using accurate PV cell models extracted from prototype measurement, we have demonstrated up to a factor of 2.36X output power improvement of a large-scale PV system comprised of three PV modules with 60 PV cells per module.

In spite of extensive research it is still quite expensive to store electrical energy without converting it to a different form of energy. As of today, no single type of electrical energy storage (EES) element can fulfill all the desirable features of an ideal storage device, e.g., high-efficiency, high-power/energy capacity, low-cost, and long-cycle life. A hybrid EES system (HEES) consists of two or more heterogeneous EES elements, realizing the advantages of each EES element while hiding their weaknesses. HEES systems exhibit superior performance compared with homogeneous EES systems when appropriate charge allocation and replacement policies are developed and used. In addition, charge migration is mandatory because the optimal EES banks for charge allocation and replacement are in general different, and each EES bank has limited storage capacity. This paper formally describes the notion of charge migration efficiency and its optimization. We first define the charge migration architecture and the corresponding charge migration optimization problem. We provide a systematic solution for the single-source, single-destination charge migration problem considering the efficiency variation of the converters, the rate capacity and internal power loss of the storage element, the terminal voltage variation of the storage elements as a function of their state of charge, and so on. We also introduce the optimal solutions for both the time-constrained and -unconstrained versions of the charge migration problem formulations. Experimental results demonstrate significant charge migration efficiency improvement of up to 83.4%.

Hybrid electrical energy storage (HEES) systems are comprised of multiple banks of heterogeneous electrical energy storage (EES) elements with distinct properties. This paper defines and solves the problem of scheduling multiple charge migration tasks in HEES systems with the objective of minimizing the total energy drawn from the source banks. The solution approach consists of two steps: (i) Finding the best charging current profile and voltage level setting for the Charge Transfer Interconnect (CTI) bus for each charge migration task, and (ii) Merging and scheduling the charge migration tasks. Experimental results demonstrate improvements of up to 32.2% in the charge migration efficiency compared to baseline setups in an example HEES system.

Electric vehicles (EV) are considered as a strong alternative of internal combustion engine vehicles expecting lower carbon emission. However, their actual benefits are not yet clearly verified while the energy efficiency can be improved in many ways. The carbon emission benefits from EV is largely diminished if we charge EV with electricity from petroleum power plants due to power loss during generation, transmission, conversion and charging. On the other hand, regenerative braking is direct power conversion from the wheel to battery and one of the most important processes that can enhance energy efficiency of EV. Power loss during regenerative braking can be reduced by hybrid energy storage system (HESS) such that supercapacitors accept high power as batteries have small rate capability.

Electrical energy systems (EESs) include energy generation, distribution, storage, and consumption, and involve many diverse components and sub-systems to implement these tasks. This paper represents a first step towards the computer-aided design for EESs, encompassing modeling, simulation, design and optimization of these systems. CAD for EESs is a challenging task that mandates a multidisciplinary and heterogeneous approach. We identify similarities and differences between electrical energy systems and electronics systems in order to inherit as much as possible the profound legacy resources of electronic design automation (EDA). We introduce fundamental concepts, from the general problem formulation to the development and deployment of efficient, scalable, and versatile CAD and EDA methods and framework for the optimal or near-optimal EESs.

Battery life of high-end smartphones and tablet PCs is becoming more and more important due to the gap between the rapid increase in power requirements of the electronic components and the slow increase in energy storage capacity of Li-ion batteries. Energy harvesting, on the other hand, is a promising technique that can prolong the battery life without compromising the users' experience with the devices and potentially without the necessity to have access to a wall AC outlet. Such energy harvesting products are available on the market today, but most of them are equipped with only a large battery pack, which exhibits poor capacity utilization during solar energy harvesting. In this paper, we propose and demonstrate that using a supercapacitor instead of a large capacity battery can be beneficial in terms of improving the charging efficiency, and thereby, significantly reducing the charging time. However, this is not a trivial task and gives rise to many problems associated with charging the supercapacitor via the USB charging port. We analyze the USB charging standard and commercial USB charger designs in smartphones to formulate an energy efficiency optimization problem and propose a dynamic programming-based online algorithm to solve the aforesaid problem. Experimental results show up to 34.5% of charging efficiency improvement compared with commercial solar charger designs.

State-of-the-art electrical energy storage (EES) systems are mainly homogeneous, i.e., they consist of a single type of EES elements. None of the existing EES elements is capable of simultaneously fulfilling all the desired features of an ideal EES system, e.g., high charge/discharge efficiency, high energy density, low cost per unit capacity, long cycle life. A novel technology, i.e., a hybrid EES system that employs heterogeneous EES elements organized in a hierarchy of storage banks and linked by appropriate charge transfer interconnects, has shown great promise in overcoming the aforesaid limitations of conventional EES systems. However, the widespread adoption/deployment of hybrid EES systems is hampered by lack of a hybrid EES system simulator. This paper thus presents SIMES, a powerful and scalable simulator for hybrid EES systems, which provides fast and accurate system simulations, while accounting for key characteristics of various EES elements, power converters, charge transfer interconnect schemes, etc. Experimental results on two different applications (one targeting load shifting for households, the other related to battery rate capacity effect minimization in portable electronic devices) demonstrate the value and usefulness of SIMES for designing energy-aware facilities and products.

Organic light emitting diode (OLED) display is a self-illuminating device that is supposed to be more power efficient than liquid crystal display (LCD). However, OLED display panels consume as much power as LCD panels due to total internal reflection. As the power consumption of the OLED panel depends on the pixel colors, most of the earlier power saving methods alter the pixel colors. In practice, such OLED power saving techniques can hardly accommodate photo viewers and movie players. This paper introduces the first OLED power saving technique that dynamically changes the supply voltage of the panel. Reduced supply voltage results in both power saving and decreased pixel luminance, but model-based color correction restores the decreased luminance with minimum color distortion. This technique is similar to dynamic backlight scaling of LCDs but is based on the unique characteristics of the OLED drivers. We provide an online color compensation algorithm using the luminance histogram. Luminance quantization in the histogram also achieves resource minimization. We develop a prototype and demonstrate the proposed OLED dynamic voltage scaling (DVS). Experimental result shows that the proposed OLED DVS saves up to 74.7% of the display power for the still images and up to 35.9% for movie clips.

A hybrid electrical energy storage (HEES) system consists of multiple banks of heterogeneous electrical energy storage (EES) elements placed between a power source and some load devices and providing charge storage and retrieval functions. For an HEES system to perform its desired functions of 1) reducing electricity costs by storing electricity obtained from the power grid at off-peak times when its price is lower, for use at peak times instead of electricity that must be bought then at higher prices, and 2) alleviating problems, such as excessive power fluctuation and undependable power supply, which are associated with the use of large amounts of renewable energy on the grid, appropriate charge management policies must be developed in order to efficiently store and retrieve electrical energy while attaining performance metrics that are close to the respective best values across the constituent EES banks in the HEES system. This paper is the first to formally describe the global charge allocation problem in HEES systems, namely, distributing a specified level of incoming power to a subset of destination EES banks so that maximum charge allocation efficiency is achieved. The problem is formulated as a mixed integer nonlinear program with the objective function set to the global charge allocation efficiency and the constraints capturing key requirements and features of the system such as the energy conservation law, power conversion losses in the chargers, the rate capacity, and self-discharge effects in the EES elements. A rigorous algorithm is provided to obtain near-optimal charge allocation efficiency under a daily charge allocation schedule. A photovoltaic array is used as an example of the power source for the charge allocation process and a heuristic is provided to predict the solar radiation level with a high accuracy. Simulation results using this photovoltaic cell array and a representative HEES system demonstrate up to 25% gain in the charge allocation efficiency by employing the proposed algorithm.

Hybrid electrical energy storage (HEES) systems are composed of multiple banks of heterogeneous electrical energy storage (EES) elements with distinctive properties. Charge replacement in a HEES system (i.e., dynamic assignment of load demands to EES banks) is one of the key operations in the system. This paper formally describes the global charge replacement (GCR) optimization problem and provides an algorithm to find the near-optimal GCR control policy. The optimization problem is formulated as a mixed-integer nonlinear programming problem, where the objective function is the charge replacement efficiency. The constraints account for the energy conservation law, efficiency of the charger/converter, the rate capacity effect, and self-discharge rates plus internal resistances of the EES element arrays. The near-optimal solution to this problem is obtained while considering the state of charges (SoCs) of the EES element arrays, characteristics of the load devices, and estimates of energy contributions by the EES element arrays. Experimental results demonstrate significant improvements in the charge replacement efficiency in an example HEES system comprised of banks of battery and supercapacitor elements with a high-power pulsed military radio transceiver as the load device.

A hybrid electrical energy storage (HEES) system that consists of multiple, heterogeneous electrical energy storage (EES) elements is a promising solution to achieve a cost-effective EES system because no storage element has ideal characteristics. The state-of-the-art HEES systems are based on a shared-bus charge transfer interconnect (CTI) architecture. Consequently, they are quite limited in scalability which is a function of the number of EES banks. This paper is the first introduction of a HEES system based on a networked CTI architecture, which is highly scalable and is capable of accommodating multiple, concurrent charge transfers. The paper starts by presenting a router architecture for the networked CTI and an effective on-line routing algorithm for multiple charge transfers. In the proposed algorithm, negotiated congestion (NC) routing for multiple charge transfers is performed and any lack of routing resources is addressed by merging two or more charge transfers while maximizing the overall energy efficiency by setting the optimal voltage level for the shared CTI. Examples of the proposed networked CTI are presented and the efficacy of the routing algorithm is demonstrated on a mesh-grid networked CTI.

The design of electric vehicles require a complete paradigm shift in terms of embedded systems architectures and software design techniques that are followed within the conventional automotive systems domain. It is increasingly being realized that the evolutionary approach of replacing the engine of a car by an electric engine will not be able to address issues like acceptable vehicle range, battery lifetime performance, battery management techniques, costs and weight, which are the core issues for the success of electric vehicles. While battery technology has crucial importance in the domain of electric vehicles, how these batteries are used and managed pose new problems in the area of embedded systems architecture and software for electric vehicles. At the same time, the communication and computation design challenges in electric vehicles also have to be addressed appropriately. This paper discusses some of these research challenges.

Hybrid electrical energy storage (HEES) systems consist of multiple banks of heterogeneous electrical energy storage (EES) elements that are connected to each other through the Charge Transfer Interconnect. A HEES system is capable of providing an electrical energy storage means with very high performance by taking advantage of the strengths (while hiding the weaknesses) of individual EES elements used in the system. Charge migration is an operation by which electrical energy is transferred from a group of source EES elements to a group of destination EES elements. It is a necessary process to improve the HEES system's storage efficiency and its responsiveness to load demand changes. This paper is the first to formally describe a more general charge migration problem, involving multiple sources and multiple destinations. The multiple-source, multiple-destination charge migration optimization problem is formulated as a nonlinear programming (NLP) problem where the goal is to deliver a fixed amount of energy to the destination banks while maximizing the overall charge migration efficiency and not depleting the available energy resource of the source banks by more than a given percentage. The constraints for the optimization problem are the energy conservation relation and charging current constraints to ensure that charge migration will meet a given deadline. The formulation correctly accounts for the efficiency of chargers, the rate capacity effect of batteries, self-discharge currents and internal resistances of EES elements, as well as the terminal voltage variation of EES elements as a function of their state of charges (SoC's). An efficient algorithm to find a near-optimal migration control policy by effectively solving the above NLP optimization problem as a series of quasi-convex programming problems is presented. Experimental results show significant gain in migration efficiency up to 35%.

Photovoltaic (PV) power generation systems are one of the most promising renewable power sources to reduce carbon footprint. Grid-connected PV power systems do not generally have a battery to store the excess charge. However, due to severe imbalance between the peak PV power generation and peak load demand, battery-less Grid-connected PV systems are much less effective for the purpose of power generation and demand mismatch mitigation. Grid-connected PV systems equipped with a battery indeed require elaborate management. This is the first paper that introduces a systematic battery management optimization that accommodates arbitrary electricity billing policies. We formulate an optimization framework to determine the battery charging current from the Grid and PV array taking into account the limited battery capacity, power converter efficiency, battery's internal resistance and rate capacity effect, and maximum power tracking of the PV array. Experimental results show that the proposed algorithm effectively reduces the electricity bill by as much as 28% when compared with previous state-of-the-art battery management policies.

A joint optimization of the physical system and the cyber world is one of the key problems in the design of a cyber-physical system (CPS). The major mechanical forces and/or chemical reactions in a plant are commonly modified by actuators in the balance-of-plant (BOP) system. More powerful actuators requires more power, but generally increase the response of the physical system powered by the electrical energy generated by the physical system. To maximize the overall output of a power generating plant therefore requires joint optimization of the physical system and the cyber world, and this is a key factor in the design of a CPS. We introduce a systematic approach to the modeling and synthesis of a CPS that emphasize joint power optimization, using an active direct methanol fuel cell (DMFC) as a case study. Active DMFC systems are superior to passive DMFCs in terms of fuel efficiency thanks to their BOP system, which includes pumps, air blowers, and fans. However, designing a small-scale active DMFC with the best overall system efficiency requires the BOP system to be jointly optimized with the DMFC stack operation, because the BOP components are powered by the stack. Our approach to this synthesis problem involves i) BOP system characterization, ii) integrated DMFC system modeling, iii) configuring a system for the maximum net power output through design space exploration, iv) synthesis of feedback control tasks, and v) implementation.

Unlike liquid crystal display (LCD) panels that require high-intensity backlight, organic LED (OLED) display panels naturally consume low power and provide high image quality thanks to their self-illuminating characteristic. In spite of this fact, the OLED display panel is still the dominant power consumer in battery-operated devices. As a result, there have been many attempts to reduce the OLED power consumption. Since power consumption of any pixel of the OLED display depends on the color that it displays, previous power saving methods change the pixel color subject to a tolerance level on the color distortion specified by the users. In practice, the OLED power saving techniques cannot be used on common user applications such as photo viewers and movie players.

Modern batteries (e.g., Li-ion batteries) provide high discharge efficiency, but the rate capacity effect in these batteries drastically decreases the discharge efficiency as the load current increases. Electric double layer capacitors, or simply supercapacitors, have extremely low internal resistance, and a battery-supercapacitor hybrid may mitigate the rate capacity effect for high pulsed discharging current. However, a hybrid architecture comprising a simple parallel connection does not perform well when the supercapacitor capacity is small, which is a typical situation because of the low energy density and high cost of supercapacitors. This paper presents a new battery-supercapacitor hybrid system that employs a constant-current charger. The constant-current charger isolates the battery from supercapacitor to improve the end-to-end efficiency for energy from the battery to the load while accounting for the rate capacity effect of Li-ion batteries and the conversion efficiencies of the converters.

Compared with the conventional homogeneous electrical energy storage (EES) systems, hybrid electrical energy storage (HEES) systems provide high output power and energy density as well as high power conversion efficiency and low self-discharge at a low capital cost. Cycle efficiency of a HEES system (which is defined as the ratio of energy which is delivered by the HEES system to the load device to energy which is supplied by the power source to the HEES system) is one of the most important factors in determining the overall operational cost of the system. Therefore, EES banks within the HEES system should be prudently designed in order to maximize the overall cycle efficiency. However, the cycle efficiency is not only dependent on the EES element type, but also the dynamic conditions such as charge and discharge rates and energy efficiency of peripheral power circuitries. Also, due to the practical limitations of the power conversion circuitry, the specified capacity of the EES bank cannot be fully utilized, which in turn results in over-provisioning and thus additional capital expenditure for a HEES system with a specified level of service. This is the first paper that presents an EES bank reconfiguration architecture aiming at cycle efficiency and capacity utilization enhancement. We first provide a formal definition of balanced configurations and provide a general reconfigurable architecture for a HEES system, analyze key properties of the balanced reconfiguration, and propose a dynamic reconfiguration algorithm for optimal, online adaptation of the HEES system configuration to the characteristics of the power sources and the load devices as well as internal states of the EES banks. Experimental results demonstrate an overall cycle efficiency improvement of by up to 108% for a DC power demand profile, and pulse duty cycle improvement of by up to 127% for high-current pulsed power profile. We also present analysis results for capacity utilization improvement for a reconfigurable EES bank.

Quality power estimation is a basis of efficient power management of electronic systems. Indirect power measurement, such as power estimation using a CPU performance monitoring unit (PMU), is widely used for its low cost and area overheads. However, the existing CPU PMUs only monitor the core and cache activities, which result in a significant accuracy limitation in the system-wide power estimation including off-chip memory devices. In this paper, we propose an on-chip bus (OCB) PMU that directly captures on-chip and off-chip component activities by snooping the OCB. The OCB PMU stores the activity information in separate counters, and online software converts counter values into actual power values with simple first-order linear power models. We also introduce an optimization algorithm that minimizes the energy model to reduce the number of counters in the OCB PMU. We compare the accuracy of the power estimation using the proposed OCB PMU with real hardware measurement and cycle-accurate system-level power estimation, and demonstrate high estimation accuracy compared with CPU PMU-based estimation method.

Direct methanol fuel cells (DMFCs) are a promising next-generation energy source for portable applications, due to their high energy density and the ease of handling of the liquid fuel. However, the limited range of output power obtainable from a fuel cell requires hybridization the introduction of a battery to form a stand-alone portable power source. Furthermore, the stringent operating conditions to be met by active DMFC systems mandate complicated balance of plant (BOP) control. We present a complete hybrid active DMFC system design and implementation in which a DMFC stack and a li-ion battery are linked by a hybridization circuit to share the applied load to exploit high energy density of the fuel cell and high power density of the battery. We describe systems for fuel delivery, air supply, temperature management, current and voltage measurement, DC-DC conversion and power distribution, motor driving, battery charge management, DMFC and circuit protection, and control of the DMFC and battery as a hybrid. We have designed and implemented an embedded system controller that consists of a 32-bit microcontroller, running under a real-time operating system, that incorporating multiple cascaded feedback control loops which manage the dynamics of BOP control. We demonstrate reliable and efficient maintenance of a constant fuel cell output current in spite of severe fluctuation of the load current.

It is important to maintain high efficiency when charging electrical energy storage elements so as to achieve holistic optimization from an energy generation source (e.g., a solar cell array) to an energy storage element (e.g., a supercapacitor bank). Previous maximum power point tracking (MPPT) methods do not consider the fact that efficiency of the charger varies depending on the power output level of the energy generation source and the state of charge of the storage element. This paper is the first paper to optimize the efficiency of a supercapacitor charging process by utilizing the MPPT technique and simultaneously considering the variable charger efficiency. More precisely, previous MPPT methods only maximize the power output of the energy generation source, but they do not guarantee the maximum energy is stored in the energy storage element. Note that the load device takes its energy from the storage element so it is important to maximize energy transfer from the source into the storage element. We present a rigorous framework to determine the optimal capacitance of a supercapacitor and optimal configuration of a solar cell array so as to maximize the efficiency of energy transfer from the solar cells into a bank of supercapacitors. Experimental results show the efficacy of the proposed technique and design optimization framework.

Modern vehicle electronics control units (ECUs) are getting rapidly complicated because of active safety and semi-autonomous driving controls, such as electric stability program (ESP) and adaptive cruise control (ACC). Furthermore, the operational environment of ECUs is extremely harsh, especially in terms of an ambient temperature well exceeding 100°C, which causes a very small temperature headroom. Thus, ECUs require a careful temperature management and high performance at the same time.

Electrical energy is a high quality form of energy that can be easily converted to other forms of energy with high efficiency and, even more importantly, it can be used to control lower grades of energy quality with ease. However, building a cost-effective electrical energy storage (EES) system is a challenging task despite steady advances in the design and manufacturing of EES elements including various battery and supercapacitor technologies. As of today, no single type of EES element fulfills high energy density, high power delivery capacity, low cost per unit of storage, long cycle life, low leakage, and so on at the same time.

In this paper we propose an on-chip bus PMU which makes accurate estimates of system power consumption from a first-order linear power model by utilizing system-level activity information exchanged on the on-chip bus. It can easily be customized for different on-chip and off-chip memory devices, and is not dependent on a specific CPU core. We model memory devices using energy state machines, describe them in XML, and use that description automatic synthesis of the PMU.We compare the short-term accuracy of the proposed PMU with a cycle-accurate system-level power estimator, and assess its long-term accuracy with a real hardware prototype. Experimental results show that the the power estimation deviates less than 5% from real measurements.

Energy-aware task scheduling significantly reduces the total energy required by a system to perform a particular job, by dynamically changing the clock frequency and supply voltage at which the CPU operates. But this causes significant fluctuation of the current drawn from the power source, so that no single voltage regulator can achieve satisfactory efficiency over the entire range of operating currents.

The lifetime of a stand-alone media recorder is a function of both the battery size and flash memory size. In this paper, we present a power management framework for media recorders that significantly enhances their lifetime while minimizing the flash memory usage and maintaining the same level of recording quality. This is achieved by implementing a mixture of encoding algorithms of different complexities that generate data with different compression ratios, and in turn balancing the energy consumption and the flash memory usage.

Recent wireless sensor nodes, equipped with ultra-low-power (ULP) RISC microcontrollers, do not generally support DVS (dynamic voltage scaling), though the ULP microcontrollers have ideal energy-voltage-frequency characteristics for DVS. In general, an output-adjustable DC-DC converter is hardly all affordable in such sensor nodes, and surprisingly light current consumption makes the DC-DC converter operate in a very inefficient region.