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Transient computing systems, also known as intermittent computing systems, are batteryless systems powered by energy harvesting (EH) sources that do not require large energy storage for system operations. Instead, they rely on retaining their state, i.e. a snapshot, in non-volatile memory (NVM) in the event of a power outage and restoring it when the power recovers. In this paper, we first discuss the limitations of state-of-the-art techniques that attempt to minimize the amount of system state saved to NVM. Therefore, we propose a novel energy-efficient system-level approach for state retention through memory tracing based on a custom hardware module named MeTra that traces changes in the main (volatile) memory between power outages. MeTra allows the voltage threshold that activates the state retention process to be dynamically adjusted according to the energy requirement of each snapshot. Thus, a great proportion of the energy harvested can be spent on useful operations. Experimental results show that the system’s active time can be extended up to 17x for Flash-based systems and 92.2% for FRAM-based systems, compared to saving the entire system state, with an area overhead of as little as 2.48%.

The emerging class of transient computing systems enables computation to be sustained despite power outages due to the variable nature of energy harvesting. However, existing approaches are largely designed for specific architectures, and hence are not broadly applicable across different IoT devices. Emerging platforms based on portable, hardware-independent software should rely on lightweight operating systems (OSs) designed specifically for embedded IoT applications, such as Arm mbed OS and Contiki OS. To enable the widespread use of transient computing, transient approaches need to be integrated into these operating systems. In this paper, we discuss the challenges of providing software primitives for transient computing to facilitate hardware-independent implementation using standard OS APIs, and present the integration of a state-of-art transient approach, Hibernus into mbed OS. This OS is chosen due to the large community of developers and the open-source IoT code availability. Transient computing is offered through a modular and layered structure that uses the available mbed OS APIs, including different strategies for retaining the system state designed for different types of flash memory. To illustrate the applicability of the proposed design, we implemented Hibernus on two mbed platforms with different flash memories, which respectively requires 4.7mF and 4.9mF of additional storage.

Intermittent computing is a new paradigm enabling battery-less computing devices to be powered directly from energy harvesting, enabling IoT devices that are free from the cost, size and lifetime constraints of batteries. To cope with frequent power interruptions, intermittent computing systems save computational progress before power is lost, and restore it when power returns. Recent research in power-neutral operation of multiprocessor system-on-chips (MPSoCs), where performance scaling is used to instantaneously match power consumption with supply, motivates the need for intermittent computing on high-performance systems. Existing works provide solutions for microcontrollers, but with the increased complexity of high-performance SoCs, new challenges such as hierarchical memory and dependence on large existing libraries emerge. In this paper, we provide a taxonomy of published intermittent computing methods and identify the most suitable method for high-performance SoCs. The chosen method is then implemented and experimentally validated on an Arm A9 out-of-order application processor. Results show that state can be saved/restored correctly in 8.6 ms for a minimal bare-metal application, which is an order of magnitude faster than the platform’s hardware boot time.

Multiprocessor systems provide both highperformance and energy-efficient execution of applications on mobile and embedded systems under dynamic workload requirements, and can provide increased lifetime for devices in energy-constrained environments. However, their increasing complexity means that management at runtime has become a non-trivial task, especially in heterogeneous multiprocessor systems. In addition, there is no standardised mechanism to expose and manage the sources of control and monitoring from within applications and hardware resources at runtime.This paper presents an analysis of applications, platforms and runtime management approaches to motivate the need for a standardised framework that enables fully applicationand platform-agnostic runtime management. The exposure of application controls and requirements through the presented framework is demonstrated with a stereo matching algorithm, including runtime management of multi-threading and frequency scaling on the 61-core Xeon Phi platform. In addition, the trading of application parameters, such as throughput and accuracy, is demonstrated within the framework using a runtime controller on the Odroid-XU3 platform. An open-source implementation of this framework has been released.

Heterogeneous multiprocessor systems have increased in complexity to provide both high performance and energy efficiency for a diverse range of applications. This motivates the need for a standard framework that enables the management, at runtime, of software applications executing on these processors. This paper proposes the first fully application- and platform-agnostic framework for runtime management approaches that control and optimise software applications and hardware resources. This is achieved by separating the system into three distinct layers connected by an API and cross-layer constructs called knobs and monitors. The proposed framework also supports the management of applications that are executing concurrently on heterogeneous platforms. The operation of the proposed framework is experimentally validated using a basic runtime controller and two heterogeneous platforms, to show how it is application- and platform-agnostic and easy to use. Furthermore, the management of concurrently executing applications through the framework is demonstrated. Finally, two recently reported runtime management approaches are implemented to demonstrate how the framework enables their operation and comparison. The energy and latency overheads introduced by the framework have been quantified and an open-source implementation has been released.

Multi-core and heterogeneous processors in modern embedded platforms have increased in complexity to provide both high-performance and energy-efficient execution of applications.As a result, the runtime management and control of these platforms has become a non-trivial process with many different approaches being reported in the literature. In addition, applications have become increasingly dynamic to exploit these processors runtime adjustable parameters that can be tuned to optimise and influence their behaviour. These two challenges motivate the need for a consistent approach to runtime management that is cross-platform and generic in the support of applications. This abstract presents the PRiME Framework, a cross-layer framework that enables application- and platformagnostic runtime management by separating a system into three distinct layers connected by an API and cross-layer constructs called knobs and monitors. The motivation for the framework’s underlying concepts are discussed and its use is demonstrated with a range of platforms and applications.An open-source implementation of this framework has been released.

Increasing energy efficiency and reliability at runtime is a key challenge of heterogeneous many-core systems. We demonstrate how contributions from the PRiME project integrate to enable application- and platform-agnostic runtime management that respects application performance targets. We consider opportunities to enable runtime management across the system stack and we enable cross-layer interactions to trade-off power and reliability with performance and accuracy. We consider a system as three distinct layers, with abstracted communication between them, which enables the direct comparison of different approaches, without requiring specific application or platform knowledge. Application-agnostic runtime management is demonstrated with a selection of runtime managers from PRiME, including linear regression modelling and predictive thermal management, operating across multiple applications. Platform-independent runtime management is demonstrated using two heterogeneous platforms.

Energy harvesters are being used to power autonomous systems, but their output power is variable and intermittent. To sustain computation, these systems integrate batteries or supercapacitors to smooth out rapid changes in harvester output. Energy storage devices require time for charging and increase the size, mass, and cost of systems. The field of transient computing moves away from this approach, by powering the system directly from the harvester output. To prevent an application from having to restart computation after a power outage, approaches such as Hibernus allow these systems to hibernate when supply failure is imminent. When the supply reaches the operating threshold, the last saved state is restored and the operation is continued from the point it was interrupted. This paper proposes Hibernus++ to intelligently adapt the hibernate and restore thresholds in response to source dynamics and system load properties. Specifically, capabilities are built into the system to autonomously characterize the hardware platform and its performance during hibernation in order to set the hibernation threshold at a point which minimizes wasted energy and maximizes computation time. Similarly, the system auto-calibrates the restore threshold depending on the balance of energy supply and consumption in order to maximize computation time. Hibernus++ is validated both theoretically and experimentally on microcontroller hardware using both synthesized and real energy harvesters. Results show that Hibernus++ provides an average 16% reduction in energy consumption and an improvement of 17% in application execution time over state-of-the-art approaches.

Energy harvesters offer the possibility for embedded IoT computing systems to operate without batteries. However, their output power is usually unpredictable and highly variable. To mitigate the effect of this variability, systems incorporate large energy buffers, increasing their size, mass and cost. The emerging class of transient computing systems differs from this approach, operating directly from the energy harvesting source and minimizing or removing additional energy storage. Existing transient approaches are largely designed for specific applications and architectures. Hence, they suffer from not being broadly applicable across multiple embedded IoT platforms. To address this challenge, transient approaches need to be integrated within a general IoT programming framework such as ARM’s mbed IoT Device Platform. This support is offered through libraries and application programming interfaces(APIs) which enable transient computing to be implemented as a service on top of IoT application protocols.

Batteries have traditionally been used to power embedded electronic devices. However, requirements such as a long lifetime, low cost, and weight, pose significant challenges to battery-powered systems. Energy harvesting offers the potential for embedded systems to operate without batteries. Nonetheless, harvesting has been traditionally coupled with large energy buffers such as supercapacitors to tackle the instability of the source. Transiently-powered computing systems enable computation to be sustained despite the sourcevariability, without the need for additional energy storage. To make this feasible, the system state (e.g. registers and RAM) needs to be saved to Non-Volatile Memory (NVM) before a power outage, and restored once power is available again. Existing transient systems save the entire state of the system upon power failure and do not consider the properties of different NVM technologies, leading into a sub-optimal state retention process. As a consequence, the time and energy spent towards useful computation are decreased significantly, affecting the forward progress that the system can achieve. The aim of this research is to introduce novel methods to reduce the time and energy overhead of the state retention process, exploring solutions both in the software and hardware domain.

Energy harvesting offers the potential for embedded systems to operate without batteries. However, harvesting has been traditionally coupled with large energy buffers such as supercapacitors to mitigate the effect of the source variability. An emerging class of transiently-powered sensing systems enable computation to be sustained during intermittent supply, without using any additional energy storage. To deal with the intermittent nature of the input source, the system state (e.g. registers and RAM) is saved to Non-Volatile Memory (NVM) before a power failure, and restored when the power supply recovers. Existing approaches save the entire state of the system upon power failure, but this is energy and time consuming. In this poster, novel selective policies for efficiently retaining state are explored, which exploit properties of different NVM technologies.

Wireless Sensor Networks (WSNs) offer a new solution for distributed monitoring, processing and communication. First of all, the stringent energy constraints to which sensing nodes are typically subjected. WSNs are often battery powered and placed where it is not possible to recharge or replace batteries. Energy can be harvested from the external environment but it is a limited resource that must be used efficiently. Energy efficiency is a key requirement for a credible WSNs design. From the power source's perspective, aggressive energy management techniques remain the most effective way to prolong the lifetime of a WSN. A new adaptive algorithm will be presented, which minimizes the consumption of wireless sensor nodes in sleep mode, when the power source has to be regulated using DC-DC converters. Another important aspect addressed is the time synchronisation in WSNs. WSNs are used for real-world applications where physical time plays an important role. An innovative low-overhead synchronisation approach will be presented, based on a Temperature Compensation Algorithm (TCA). The last aspect addressed is related to self-powered WSNs with Energy Harvesting (EH) solutions. Wireless sensor nodes with EH require some form of energy storage, which enables systems to continue operating during periods of insufficient environmental energy. However, the size of the energy storage strongly restricts the use of WSNs with EH in real-world applications. A new approach will be presented, which enables computation to be sustained during intermittent power supply. The discussed approaches will be used for real-world WSN applications. The first presented scenario is related to the experience gathered during an European Project (3ENCULT Project), regarding the design and implementation of an innovative network for monitoring heritage buildings. The second scenario is related to the experience with Telecom Italia, regarding the design of smart energy meters for monitoring the usage of household's appliances.