Intermittent Computing: Challenges and Opportunities

The maturation of energy-harvesting technology and ultra-low-power computer systems has led to the advent of intermittently-powered, batteryless devices that operate entirely using energy extracted from their environment. Intermittently operating devices present a rich vein of programming languages research challenges and the purpose of this paper is to illustrate these challenges to the PL research community. To provide depth, this paper includes a survey of the hardware and software design space of intermittent computing platforms. On the foundation of these research challenges and the state of the art in intermittent hardware and software, this paper describes several future PL research directions, emphasizing a connection between intermittence, distributed computing, energy-aware programming and compilation, and approximate computing. We illustrate these connections with a discussion of our ongoing work on programming for intermittence, and on building and simulating intermittent distributed systems

Sophisticated Batteryless Sensing

Wireless embedded sensing systems have revolutionized scientific, industrial, and consumer applications. Sensors have become a fixture in our daily lives, as well as the scientific and industrial communities by allowing continuous monitoring of people, wildlife, plants, buildings, roads and highways, pipelines, and countless other objects. Recently a new vision for sensing has emerged---known as the Internet-of-Things (IoT)---where trillions of devices invisibly sense, coordinate, and communicate to support our life and well being. However, the sheer scale of the IoT has presented serious problems for current sensing technologies---mainly, the unsustainable maintenance, ecological, and economic costs of recycling or disposing of trillions of batteries. This energy storage bottleneck has prevented massive deployments of tiny sensing devices at the edge of the IoT. This dissertation explores an alternative---leave the batteries behind, and harvest the energy required for sensing tasks from the environment the device is embedded in. These sensors can be made cheaper, smaller, and will last decades longer than their battery powered counterparts, making them a perfect fit for the requirements of the IoT. These sensors can be deployed where battery powered sensors cannot---embedded in concrete, shot into space, or even implanted in animals and people. However, these batteryless sensors may lose power at any point, with no warning, for unpredictable lengths of time. Programming, profiling, debugging, and building applications with these devices pose significant challenges. First, batteryless devices operate in unpredictable environments, where voltages vary and power failures can occur at any time---often devices are in failure for hours. Second, a device\u27s behavior effects the amount of energy they can harvest---meaning small changes in tasks can drastically change harvester efficiency. Third, the programming interfaces of batteryless devices are ill-defined and non- intuitive; most developers have trouble anticipating the problems inherent with an intermittent power supply. Finally, the lack of community, and a standard usable hardware platform have reduced the resources and prototyping ability of the developer. In this dissertation we present solutions to these challenges in the form of a tool for repeatable and realistic experimentation called Ekho, a reconfigurable hardware platform named Flicker, and a language and runtime for timely execution of intermittent programs called Mayfly

Constraint Based Compiler Optimization for Energy Harvesting Applications

We propose a method for optimizing the energy efficiency of software code running on small computing devices in the Internet of Things (IoT) that are powered exclusively by electricity harvested from ambient energy in the environment. Due to the weak and unstable nature of the energy source, it is challenging for developers to manually optimize the software code to deal with mismatch between the intermittent power supply and the computation demand. Our method overcomes the challenge by using a combination of three techniques. First, we use static program analysis to automatically identify opportunities for precomputation, i.e., computation that may be performed ahead of time as opposed to just in time. Second, we optimize the precomputation policy, i.e., a way to split and reorder steps of a computation task in the original software to match the intermittent power supply while satisfying a variety of system requirements; this is accomplished by formulating energy optimization as a constraint satisfiability problem and then solving the problem using an off-the-shelf SMT solver. Third, we use a state-of-the-art compiler platform (LLVM) to automate the program transformation to ensure that the optimized software code is correct by construction. We have evaluated our method on a large number of benchmark programs, which are C programs implementing secure communication protocols that are popular for energy-harvesting IoT devices. Our experimental results show that the method is efficient in optimizing all benchmark programs. Furthermore, the optimized programs significantly outperform the original programs in terms of energy efficiency and latency, and the overall improvement ranges from 2.3X to 36.7X

The Case for Approximate Intermittent Computing

We present the concept of approximate intermittent computing and concretely demonstrate its application. Intermittent computations stem from the erratic energy patterns caused by energy harvesting: computations unpredictably terminate whenever energy is insufficient and the application state is lost. Existing solutions maintain equivalence to continuous executions by creating persistent state on non-volatile memory, enabling stateful computations to cross power failures. The performance penalty is massive: system throughput reduces while energy consumption increases. In contrast, approximate intermittent computations trade the accuracy of the results for sparing the entire overhead to maintain equivalence to a continuous execution. This is possible as we use approximation to limit the extent of stateful computations to the single power cycle, enabling the system to completely shift the energy budget for managing persistent state to useful computations towards an immediate approximate result. To this end, we effectively reverse the regular formulation of approximate computing problems. First, we apply approximate intermittent computing to human activity recognition. We design an anytime variation of support vector machines able to improve the accuracy of the classification as energy is available. We build a hw/sw prototype using kinetic energy and show a 7x improvement in system throughput compared to state-of-the-art system support for intermittent computing, while retaining 83% accuracy in a setting where the best attainable accuracy is 88%. Next, we apply approximate intermittent computing in a sharply different scenario, that is, embedded image processing, using loop perforation. Using a different hw/sw prototype we build and diverse energy traces, we show a 5x improvement in system throughput compared to state-of-the-art system support for intermittent computing, while providing an equivalent output in 84% of the cases

Approximate Query Service on Autonomous IoT Cameras

Elf is a runtime for an energy-constrained camera to continuously summarize video scenes as approximate object counts. Elf's novelty centers on planning the camera's count actions under energy constraint. (1) Elf explores the rich action space spanned by the number of sample image frames and the choice of per-frame object counters; it unifies errors from both sources into one single bounded error. (2) To decide count actions at run time, Elf employs a learning-based planner, jointly optimizing for past and future videos without delaying result materialization. Tested with more than 1,000 hours of videos and under realistic energy constraints, Elf continuously generates object counts within only 11% of the true counts on average. Alongside the counts, Elf presents narrow errors shown to be bounded and up to 3.4x smaller than competitive baselines. At a higher level, Elf makes a case for advancing the geographic frontier of video analytics

Designing, Fabricating and TestingConcurrently Active Wireless Sensors

There are many possible uses for remotely powered environmental sensing devices. The University of Pittsburgh has obtained a subcontract to assist in the first development phase of one such device for NASA, a wireless temperature sensor that could ultimately be used to measure the temperature of panels on their spacecraft. This thesis describes the work that has been done to completely meet the project specifications set forth in the subcontract, with particular emphasis being given to the contributions made by the author. In addition to the remote sensor board hardware and software, an embedded protocol is developed that can allow hundreds of these devices to transmit their temperature readings over a single communications channel (amplitude shift keying at 418 MHz) without interference or the need for an on-board receiver. Laboratory testing results that verify the proper operation of the final prototype are included

Energy-Efficient System Architectures for Intermittently-Powered IoT Devices

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. With no cord for power and limited space for a battery, powering these devices for operating in a set-and-forget mode (i.e., achieve several months to possibly years of unattended operation) becomes a daunting challenge. Environmental energy harvesting (where the system powers itself using energy that it scavenges from its operating environment) has been shown to be a promising and viable option for powering these IoT devices. However, ambient energy sources (such as vibration, wind, RF signals) are often minuscule, unreliable, and intermittent in nature, which can lead to frequent intervals of power loss. Performing computations reliably in the face of such power supply interruptions is challenging