Intermittent systems powered by ambient energy harvesting are becoming popular for the benefits of an infinite lifetime and minimum maintenance requirements. Nonvolatile processors (NVPs) enable continual task executions under an unstable power supply with an efficient reactive checkpointing strategy. However, recovering concurrent peripherals in an intermittent system may incur significant overhead once power failures take place, and the recovery of interrupts also lacks discussion in existing works. Noticing the different optimization directions between responsive checkpointing within NVPs and proactive checkpointing required to recover concurrent peripherals, this paper proposes REMARK, an NVP architecture enabling hybrid checkpointing and efficient peripheral recovery. REMARK expands the current NVP structure with a hybrid backup/restore module, a peripheral handler and an interrupt handler, which addresses the recovery problem of both peripherals and interrupts efficiently. A REMARK chip is fabricated to verify the proposed architecture. Results show that the execution efficiency is improved by$13\times $compared with the state-of-the-art. With programming optimization, another 36.5% performance improvement can be achieved.

Nonvolatile processors have emerged as one of the promising solutions for energy harvesting scenarios, among which Wireless Sensor Networks (WSN) provide some of the most important applications. In a typical distributed sensing system, due to difference in location, energy harvester angles, power sources, etc. different nodes may have different amount of energy ready for use. While prior approaches have examined these challenges, they have not done so in the context of the features offered by nonvolatile computing approaches, which disrupt certain foundational assumptions. We propose a new set of nonvolatility-exploiting optimizations and embody them in the NEOFog system architecture. We discuss shifts in the tradeoffs in data and program distribution for nonvolatile processing-based WSNs, showing how non-volatile processing and non-volatile RF support alter the benefits of computation and communication-centric approaches. We also propose a new algorithm specific to nonvolatile sensing systems for load balancing both computation and communication demands. Collectively, the NV-aware optimizations in NEOFog increase the ability to perform in-fog processing by 4.2X and can increase this to 8X if virtualized nodes are 3X multiplexed.

Intelligent computing and maintenance-free powering are two desirable characteristics of wearable IoT devices. Energy harvesting nonvolatile intelligent processor (NIP) with neural network computation capability has the potential to advance these goals. Individual ultraviolet (UV) exposure monitoring progressively becomes one conspicuous application of wearable devices. In resource constrained wearable sensor nodes, we can alleviate the data transmission burden via convolutional neural networks (CNNs) based pattern recognition. Nevertheless, in spite of the substantially improved computing capability of NIP, typically computational and memory intensive CNNs are still too bulky for on-node implementation. We develop an CNN-based pattern recognition system for nonvolatile IoT platform for smart UV monitoring, and propose a optimization method to achieve extremely tiny and efficient CNNs. Experimental results show that the offline-trained CNN can recognize individual UV exposure patterns with accuracy of 85%, and the simplified on-node CNN can achieve 93.2% parameters reduction with only 5% accuracy loss.

Convolutional Neural Network (CNN) has become one of the most successful technologies for visual classification and other applications. As CNN models continue to evolve and adopt different kernel sizes in various applications, it is necessary for the hardware architecture to support reconfigurability. Previous FPGAs and programmable ASICs are fine-grained reconfigurable but with energy efficiency compromise. Considering specific features of CNNs, this paper presents an energy efficient coarse-grained reconfigurable architecture, denoted as CORAL. An application-specific configuration neural block is proposed for convolution operations with reconfigurable data quantization to reduce both energy consumption and on-chip memory requirements. An optimal data loading strategy is presented for CORAL to achieve the best energy efficiency. Experimental results show that CORAL improves 80.0% energy efficiency while reduces 78.9% chip area and 81.0% reconfiguration time compared with the best up-to-date programmable ASIC solution.

Batteryless IoT devices powered through energy harvesting face a fundamental imbalance between the potential volume of collected data and the amount of energy available for processing that data locally. However, many such devices perform similar operations across each new input record, which provides opportunities for mining the potential information in buffered historical data, at potentially lower effort, while processing new data rather than abandoning old inputs due to limited computational energy. We call this approach incidental computing, and highlight synergies between this approach and approximation techniques when deployed on a non-volatile processor platform (NVP). In addition to incidental computations, the backup and restore operations in an incidental NVP provide approximation opportunities and optimizations that are unique to NVPs.

Solar-powered sensor nodes (SCSNs) with energy storages have the greatest potential and are widely used in the coming era of the Internet of Things, since they avoid tedious battery maintenance tasks. However, because the solar energy source is unstable and limited, the sensor nodes suffer from high deadline miss ratio (DMR). To achieve better DMR, the existing scheduling algorithms find the best scheduling scheme in a single period of the recurring task queue and, hence, ignore the long-term performance. To tackle this challenge, this paper proposes a three-level dynamic voltage-frequency scaling (DVFS)-based scheduling strategy to minimize long-term DMR for dual-channel SCSNs. This approach includes a day-level scheduler to achieve a coarse-grained task arrangement, two artificial neural networks to determine the task priorities, and a DVFS-based task selection algorithm for slot-level execution. Experiments show that the proposed scheduler reduces DMR by over 30% on average.

Field-programmable gate arrays (FPGAs) have drawn lots of attentions due to their programmability and high performance. Recently, ultralow-power FPGAs for Internet of Things, together with energy-harvesting technique, have become an emerging self-powered computing platform. However, volatile memory in FPGA will lose their states under unstable power supplies and cannot work efficiently. Nonvolatile FPGA becomes a promising alternative. This paper proposes a hardware/software codesign nonvolatile FPGA with efficient offline/online checkpointing strategy (CP-FPGA). Backup energy is reduced by offline selecting proper checkpointing locations to minimize backup data. An online scheduler is further proposed to balance computation rollback overhead against backup energy. Experimental results show that the proposed CP-FPGA reduces 39.5% energy consumption on average compared with the state-of-the-art techniques.

Wearable devices begin to integrate into the daily lives along with recent technology development. One of such important applications is to accurately monitor ultraviolet (UV) radiation received by the human body. To compensate for the localized monitoring area of existing personal UV monitoring devices, this paper proposes a reconstruction method to estimate the UV dose over the entire body based on multiple discrete wearable UV sensor nodes. Ambient factors and individual factors are both considered in this paper. The proposed estimation method is validated by a range of UV data collection experiments in realistic scenarios. Experimental results show that the proposed method reduces 68.3% estimation errors on average compared with existing single sensor based methods.

Nonvolatile processors have manifested strong vitality in battery-less energy harvesting sensor nodes due to their characteristics of zero standby power, resilience to power failures and fast read/write operations. However, I/O and sensing operations cannot store their system states after power off, hence they are sensitive to power failures and high power switching overhead is induced during power oscillation, which significantly degrades the system performance. In this paper, we propose a novel performance-aware task scheduling technique considering power switching overhead for energy harvesting nonvolatile processors. We first give the analysis of the power switching overhead on energy harvesting sensor nodes. Then, the scheduling problem is formulated by MILP (Mixed Integer Linear Programming). Furthermore, a task splitting strategy is adopted to improve the performance and an heuristic scheduling algorithm is proposed to reduce the problem complexity. Experimental results show that the proposed scheduling approach can improve the performance by 14% on average compared to the state-of-the-art scheduling strategy. With the employment of the task splitting approach, the execution time can be further reduced by 10.6%.

Energy harvesting is under intense investigation as a promising substitute for batteries. However, given the erratic nature of ambient energy sources, temporary status in conventional CMOS circuits can be lost upon a sudden power outage. Taking advantage of emerging nonvolatile memories (NVMs), nonvolatile processor (NVP) backs up system contexts when power failure occurs, and recalls pre-stored data on resumption. It has become a hot topic for the capability to survive power variations and to guarantee forward progress on computation tasks. This paper acts as a brief guide introducing the concepts, the current status, the challenges and opportunities, as well as emerging applications of NVPs. Through this paper, we expect to help researchers who are new in this area better understand the development trends and future research prospects. We also hope to attract researchers to join and explore more innovative applications of NVP.

With the advent of the era of trillion sensors, solar-powered sensor nodes are widely used as they do not require battery charging or replacement. However, the limited and intermittent solar energy supply seriously affects deadline miss rate (DMR) of tasks. Furthermore, traditional solar-powered sensor nodes also suffer from energy loss of battery charging and voltage conversion. Recently, a storage-less and converter-less power supply architecture has been proposed to achieve higher energy efficiency by removing the leaky energy storage and dc voltage conversion. Without energy storages, a node using inter-task scheduling is more sensitive to solar variations, which results in high DMRs. This paper proposes an intra-task scheduling scheme for the storage-less and converter-less solar-powered sensor nodes, whose features include power prediction based on classified solar profiles, a trigger mechanism to select scheduling points, an artificial neural network to calculate task priorities and a fine-grained task selection algorithm. Experimental results show that the proposed algorithm reduces DMR by up to 30% and improves energy utilization efficiency by 20% with trivial energy overheads.

Solar-powered sensor nodes with energy storages are widely used today and promising in the coming trillion sensor era, as they do not require manual battery charging or replacement. The changeable and limited solar power supply seriously affects the deadline miss rates (DMRs) of tasks on these nodes and therefore energy-driven task scheduling is necessary. However, current algorithms focus on the single period (or the current task queue) for high energy utilization and suffer from bad long term DMR. To get better long term DMR, we propose a long term deadline-aware scheduling algorithm with energy migration strategies for distributed super capacitors. Experimental results show that the proposed algorithm reduces the DMR by 27.8% and brings less than 3% of the total energy consumption.