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Batteryless, energy-harvesting systems could reshape the Internet of Things into a more sustainable societal infrastructure. 

Task-based intermittent software systems always re-execute peripheral input/output (I/O) operations upon power failures since tasks have all-or-nothing semantics. Re-executed I/O wastes significant time and energy and risks memory inconsistency. This paper presents EaseIO, a new task-based intermittent system that remedies these problems. EaseIO programming interface introduces re-execution semantics for I/O operations to facilitate safe and efficient I/O management for intermittent applications. EaseIO compiler front-end considers the programmer-annotated I/O re-execution semantics to preserve the task's energy efficiency and idem-potency. EaseIO runtime introduces regional privatization to eliminate memory inconsistency caused by idempotence bugs. Our evaluation shows that EaseIO reduces the wasted useful I/O work by up to 3× and total execution time by up to 44% by avoiding 76% of the redundant I/O operations, as compared to the state-of-the-art approaches for intermittent computing. Moreover, for the first time, EaseIO ensures memory consistency during DMA-based I/O operations. 

Today's smart devices have short battery lifetimes, high installation and maintenance costs, and rapid obsolescence - all leading to the explosion of electronic waste in the past two decades. These problems will worsen as the number of connected devices grows to one trillion by 2035. Energy harvesting, battery-free devices offer an alternative. Getting rid of the battery reduces e-waste, promises long lifetimes, and enables deployment in new applications and environments. Unfortunately, developing sophisticated inference-capable applications is still challenging. The lack of platform support for advanced (32-bit) microprocessors and specialized accelerators, which can execute dataintensive machine-learning tasks, has held back batteryless devices. 

ABSTRACT A trade-off between locomotor speed and endurance occurs in various taxa, and is thought to be underpinned by a muscle-level trade-off. Among four replicate high runner (HR) lines of mice, selectively bred for voluntary wheel-running behavior, a negative correlation between average running speed and time spent running has evolved. We hypothesize that this trade-off is due to changes in muscle physiology. We studied the HR lines at generation 90, at which time one line (L3) is fixed for the mini-muscle phenotype, another is polymorphic (L6) and the others (L7, L8) lack mini-muscle individuals. We used in situ preparations to quantify the contractile properties of the triceps surae muscle complex. Maximal shortening velocity varied significantly, being lowest in mini-muscle mice (L3 mini=25.2 mm s−1, L6 mini=25.5 mm s−1), highest in normal-muscle mice L6 and L8 (40.4 and 50.3 mm s−1, respectively) and intermediate in normal-muscle L7 mice (37.2 mm s−1). Endurance, measured both as the slope of the decline in force and the proportion of initial force that could be sustained, also varied significantly. The slope was shallowest in mini-muscle mice (L3 mini=−0.00348, L6 mini=−0.00238), steepest in lines L6 and L8 (−0.01676 and −0.01853), and intermediate in L7 (−0.01145). Normalized sustained force was highest in mini-muscle mice (L3 mini=0.98, L6 mini=0.92) and lowest in L8 (0.36). There were significant, negative correlations between velocity and endurance metrics, indicating a muscle-level trade-off. However, this muscle-level trade-off does not seem to underpin the organismal-level speed and endurance trade-off previously reported as the ordering of the lines is reversed: the lines that run the fastest for the least time have the lowest muscle complex velocity and highest endurance. 

Battery-free and intermittently powered devices offer long lifetimes and enable deployment in new applications and environments. Unfortunately, developing sophisticated inference-capable applications is still challenging due to the lack of platform support for more advanced (32-bit) microprocessors and specialized accelerators---which can execute data-intensive machine learning tasks, but add complexity across the stack when dealing with intermittent power. We present Protean to bridge the platform gap for inference-capable battery-free sensors. Designed for runtime scalability, meeting the dynamic range of energy harvesters with matching heterogeneous processing elements like neural network accelerators. We develop a modular "plug-and-play" hardware platform, SuperSensor, with a reconfigurable energy storage circuit that powers a 32-bit ARM-based microcontroller with a convolutional neural network accelerator. An adaptive task-based runtime system, Chameleon, provides intermittency-proof execution of machine learning tasks across heterogeneous processing elements. The runtime automatically scales and dispatches these tasks based on incoming energy, current state, and programmer annotations. A code generator, Metamorph, automates conversion of ML models to intermittent safe execution across heterogeneous compute elements. We evaluate Protean with audio and image workloads and demonstrate up to 666x improvement in inference energy efficiency by enabling usage of modern computational elements within intermittent computing. Further, Protean provides up to 166% higher throughput compared to non-adaptive baselines. 

Hands-on computing has emerged as an exciting and accessible way to learn about computing and engineering in the physical world for students and makers of all ages. Current end-to-end approaches like Microsoft MakeCode require tethered or battery-powered devices like a micro:bit, limiting usefulness and applicability, as well as abdicating responsibility for teaching sustainable practices. Unfortunately, energy harvesting computing devices are usually only programmable by experts and require significant supporting toolchains and knowledge across multiple engineering and computing disciplines to work effectively. This paper bridges the gap between sustainable computing efforts, the maker movement, and novice-focused programming environments with MakeCode-Iceberg, a set of compiler extensions to Microsoft's open-source MakeCode project. The extensions automatically and invisibly transform user code in any language supported (Blocks, JavaScript, Python)into a version that can safely and correctly execute across intermittent power failures caused by unreliable energy harvesting. Determining where, when, and what to save in a checkpoint on limited energy, time, and hardware budget is challenging. We leverage the unique intermediate representation of the MakeCode source-to-source compiler to design and deploy various checkpointing techniques. Our approach allows us to provide, for the first time, a fully web-based and toolchain-free environment to program intermittent computing devices, making battery-free operation accessible to all. We demonstrate new use cases with multiple energy harvesters, peripherals, and application domains: including a Smart Terrarium, Step Counter, and Combination Lock. MakeCode-Iceberg provides sustainable hands-on computing opportunities to a broad audience of makers and learners, democratizing access to energy harvesting and battery-free embedded systems. 

The COVID-19 pandemic has dramatically increased the use of face masks across the world. Aside from physical distancing, they are among the most effective protection for healthcare workers and the general population. Face masks are passive devices, however, and cannot alert the user in case of improper fit or mask degradation. Additionally, face masks are optimally positioned to give unique insight into some personal health metrics. Recognizing this limitation and opportunity, we present FaceBit: an open-source research platform for smart face mask applications. FaceBit's design was informed by needfinding studies with a cohort of health professionals. Small and easily secured into any face mask, FaceBit is accompanied by a mobile application that provides a user interface and facilitates research. It monitors heart rate without skin contact via ballistocardiography, respiration rate via temperature changes, and mask-fit and wear time from pressure signals, all on-device with an energy-efficient runtime system. FaceBit can harvest energy from breathing, motion, or sunlight to supplement its tiny primary cell battery that alone delivers a battery lifetime of 11 days or more. FaceBit empowers the mobile computing community to jumpstart research in smart face mask sensing and inference, and provides a sustainable, convenient form factor for health management, applicable to COVID-19 frontline workers and beyond.