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

Mark Weiser predicted in 1991 that computing would lead to individuals interacting with countless computing devices, seamlessly integrating them into their daily lives until they disappear into the background. However, achieving this seamless integration while addressing the associated environmental concerns is challenging. Trillions of smart devices with varied capabilities and form-factor are needed to build a networked environment of this magnitude. Yet, conventional computing paradigms require plastic housings, PCB boards, and rare-earth minerals, coupled with hazardous waste, and challenging reclamation and recycling, leading to significant e-waste. The current linear lifecycle design of electronic devices does not allow circulation among different life stages, neglecting features like recyclability and repairability during the design process. In this position paper, we present the concept of computational materials designed for transiency as a substitute for current devices. We envision that not all devices must be designed with performance, robustness, or even longevity as the sole goal. We detail computer systems challenges to the circular economy of computational materials and provide strategies and sketches of tools to assess a device's entire lifetime environmental impact. 

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. 

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. 

Wearables are a potentially vital mechanism for individuals to monitor their health, track behaviors, and stay connected. Unfortunately, both price and a lack of consideration of the needs of low-SES communities have made these devices inaccessible and unusable for communities that would most substantially benefit from their affordances. To address this gap and better understand how members of low-SES communities perceive the potential benefits and barriers to using wearable devices, we conducted 19 semi-structured interviews with people from minority, high crime rate, low-SES communities. Participants emphasized a critical need for safety-related wearable devices in their communities. Still, existing tools do not yet address the specific needs of this community and are out of reach due to several barriers. We distill themes on perceived useful features and ongoing obstacles to guide a much-needed research agenda we term ’Equityware’: building wearable devices based on low-SES communities’ needs, comfortability, and limitations. 

We have witnessed explosive growth in computing devices at all scales, in particular with small wireless devices that can permeate most of our physical world. The IoT industry is helping to fuel this insatiable desire for more and more data. We have to balance this growth with an understanding of its environmental impact. Indeed, the ENSsys community must take leadership in putting sustainability up front as a primary design principle for the future of IoT and related areas, expanding the research mandate beyond the intricacies of the computing systems in isolation to encompass and integrate the materials, new applications, and circular lifecycle of electronics in the IoT. Our call to action is seeded with a circularity-focused computing agenda that demands a cross-stack research program for energy-harvesting computational things. 

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. 

Intermittently operating embedded computing platforms powered by energy harvesting require software frameworks to protect from errors caused by Write After Read (WAR) dependencies. A powerful method of code protection for systems with non-volatile main memory utilizes compiler analysis to insert a checkpoint inside each WAR violation in the code. However, such software frameworks are oblivious to the code structure---and therefore, inefficient---when many consecutive WAR violations exist. Our insight is that by transforming the input code, i.e., moving individual write operations from unique WARs close to each other, we can significantly reduce the number of checkpoints. This idea is the foundation for WARio: a set of compiler transformations for efficient code generation for intermittent computing. WARio, on average, reduces checkpoint overhead by 58%, and up to 88%, compared to the state of the art across various benchmarks.