Batteryless wearables use energy harvested from the environment, eliminating the burden of charging or replacing batteries. This makes them convenient and environmentally friendly. However, these benefits come at a price. Batteryless wearables operate intermittently (based on energy availability), which adds complexity to their design and introduces usability limitations not present in their battery-powered counterparts. In this paper, we conduct a scenario-based study with 400 wearable users to explore how users perceive the inherent trade-offs of batteryless wearable devices. Our results reveal users’ concerns, expectations, and preferences when transitioning from battery-powered to batteryless wearable use. We discuss how the findings of this study can inform the design of usable batteryless wearables.

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.

Tsetlin Machine (TM) is a new machine learning algorithm that encodes propositional logic into learning automata---a set of logical expressions composed of boolean input features---to recognise patterns. The simplicity, efficiency, and accuracy of this logic-based algorithm encourage rethinking the application of traditional arithmetic-based neural networks (NNs) in intelligent sensors design. Indeed, TM is a promising candidate for embedding intelligence into tiny batteryless sensors with the potential to address two critical challenges: (1) computing under resource constraints and (2) demand for dynamic adaptation to the unpredictable nature of harvested energy. However, its structural model complexity manifests in two conflicting issues: large memory footprint and long latency. This paper addresses these shortcomings by proposing adaptive compression techniques exploiting the inherent redundancies observed in trained models. Through dynamically scaling the computational complexity based on available energy, our techniques significantly reduce the memory footprint and speed up the runtime execution. We evaluate our techniques against standard TMs and binarized neural networks (BNNs) for vision and acoustic workloads deployed on a TI MSP430 MCU operating under intermittent power supply conditions. We show that our techniques can achieve up to 99% compression of TM models and offer 13.5× latency and energy reductions when compared with the most efficient neural network configuration without compromising accuracy.

Tiny energy harvesting sensors that operate intermittently, without batteries, have become an increasingly appealing way to gather data in hard to reach places at low cost. Frequent power failures make forward progress, data preservation and consistency, and timely operation challenging. Unfortunately, state-of-the-art systems ask the programmer to solve these challenges, and have high memory overhead, lack critical programming features like pointers and recursion, and are only dimly aware of the passing of time and its effect on application quality. We present Time-sensitive Intermittent Computing System (TICS), a new platform for intermittent computing, which provides simple programming abstractions for handling the passing of time through intermittent failures, and uses this to make decisions about when data can be used or thrown away. Moreover, TICS provides predictable checkpoint sizes by keeping checkpoint and restore times small and reduces the cognitive burden of rewriting embedded code for intermittency without limiting expressibility or language functionality, enabling numerous existing embedded applications to run intermittently.

Transiently powered computers (TPCs) form the foundation of the battery-less Internet of Things, using energy harvesting and small capacitors to power their operation. This kind of power supply is characterized by extreme variations in supply voltage, as capacitors charge when harvesting energy and discharge when computing. We experimentally find that these variations cause marked fluctuations in clock speed and power consumption . Such a deceptively minor observation is overlooked in existing literature. Systems are thus designed and parameterized in overly conservative ways, missing on a number of optimizations. We rather demonstrate that it is possible to accurately model and concretely capitalize on these fluctuations. We derive an energy model as a function of supply voltage and prove its use in two settings. First, we develop EPIC, a compile-time energy analysis tool. We use it to substitute for the constant power assumption in existing analysis techniques, giving programmers accurate information on worst-case energy consumption of programs. When using EPIC with existing TPC system support, run-time energy efficiency drastically improves, eventually leading up to a 350% speedup in the time to complete a fixed workload. Further, when using EPIC with existing debugging tools, it avoids unnecessary program changes that hurt energy efficiency. Next, we extend the MSPsim emulator and explore its use in parameterizing a different TPC system support. The improvements in energy efficiency yield up to more than 1000% time speedup to complete a fixed workload.

Transiently-powered computers (TPCs) lay the basis for a battery-less Internet of Things, using energy harvesting and small capacitors to power their operation. This power supply is characterized by extreme variations in supply voltage, as capacitors charge when harvesting energy and discharge when computing. We experimentally find that these variations cause marked fluctuations in clock speed and power consumption, which determine energy efficiency. We demonstrate that it is possible to accurately model and concretely capitalize on these fluctuations. We derive an energy model as a function of supply voltage and develop EPIC, a compile-time energy analysis tool. We use EPIC to substitute for the constant power assumption in existing analysis techniques, giving programmers accurate information on worst-case energy consumption of programs. When using EPIC with existing TPC system support, run-time energy efficiency drastically improves, eventually leading up to a 350% speedup in the time to complete a fixed workload. Further, when using EPIC with existing debugging tools, programmers avoid unnecessary program changes that hurt energy efficiency.

We propose a new abstraction for understanding energy harvesting behaviors in the wild, especially how these behaviors impact energy constrained and battery-free sensors. The Energy Harvesting Mode abstraction explores ways to make sense of energy harvesting behaviors. We take known energy harvesting datasets, and create a few of our own, then classify energy harvesting behavior into modes. Modes are periodic or repeated elements caused by systematic or fundamental attributes of the energy harvesting environment. We show the existence of these Energy Harvesting Modes using real world data and IV surfaces created with the Ekho emulator. We discuss the impacts and usage of this powerful abstraction, including enabling adaptation, test case generation, and efficiency analysis for energy harvesting and intermittently powered sensing devices.

Emerging countries predominantly rely on room-level air conditioning units (window ACs, space heaters, ceiling fans) for thermal comfort. These distributed units have manual, decentralized control leading to suboptimal energy usage for two reasons: excessive setpoints by individuals and inability to interleave different conditioning units for energy savings. We propose a novel inverted HVAC approach: cheaply retrofitting these distributed units with “on-off” control and providing centralized control augmented with room and environmental sensors. Our binary control approach exploits an understanding of device consumption characteristics and factors this into the control algorithms to reduce consumption. We implement this approach as H awadaar in a prototype 180ft 2 room to evaluate its efficacy over a 7-month period experiencing both hot and cold climates. Through a post analysis, we show that our on-off algorithms are not far from a theoretically optimal approach based on a priori information that precisely knows the optimal control points to minimize consumption. We collect enough evidence to plausibly scale our empirical evaluation, demonstrating countrywide benefits: with just 20% market penetration, H awadaar can save up to 6% of electricity per capita in residential and commercial sectors—resulting in a substantial countrywide impact.