Object detection is essential in Computer Vision and is widely applied in all areas. This paper proposes a method called BAFPN. BAFPN is a new bidirectional Feature Pyramid Network that constructs accurate object detection networks based on YOLOv4 by implementing Adaptively Spatial Feature Fusion. Besides, Exponential Moving Average is used to improve the network performance. The developed network not only maintains high computing speed but also enhances the mAP by 4.3% when testing with the MS COCO dataset and when comparing it to the original YOLOv4. To further improve the performance, the trained model was pruned using the Batch Normalization layer's scaling factor, achieving an 18% reduction in size and improving object detection speed.

Nonvolatile static random access memory (nvSRAM) has been widely investigated as a promising on-chip memory architecture in energy harvesting sensor nodes, due to zero standby power, resilience to power failures, and fast read/write operations. However, conventional approaches back up all data from static random access memory into nonvolatile memory when power failures happen. It leads to significant energy overhead and peak inrush current, which has a negative impact on the system performance and circuit reliability. This paper proposes a holistic data backup optimization to mitigate these problems in nvSRAM, consisting of a partial backup algorithm and a run-time adaptive write policy. A statistic dead-block predictor is employed to achieve dead block identification with trivial hardware overhead. An adaptive policy is used to switch between write-back and write-through strategy to reduce the rollback induced by backup failures. Experimental results show that the proposed scheme improves the performance by 4.6% on average while the backup power consumption and the inrush current are reduced by 38.1% and 54% on average compared to the full backup scheme. What is more, the backup capacitor size for energy buffer can be reduced by 40% on average under the same performance constraint.

Nonvolatile processors (NVPs) preserve run-time information when power failure occurs by utilizing nonvolatile memory technologies. This feature enables NVPs to make forward progress continuously under intermittent power supply in energy harvesting systems. This paper builds a gem5 based NVP simulator named NVPsim, which is validated against measured results of a fabricated prototype with reasonable error rate. Furthermore, to demonstrate the capability of NVPsim for architecture exploration, we evaluated performance and energy consumption of different NVP designs varying in the choice of nonvolatile memory for on-chip caches, the backup strategy and the energy buffer size. Experimental results indicate that nvSRAM outperforms other types of nonvolatile memory as the on-chip cache for energy harvesting systems.

With the booming trend of internet of things (IoT), reconfigurable devices, such as FPGAs, have drawn lots of attentions due to their flexible and high-performance capability. However, commercial FPGAs suffer from high leakage power consumption, which makes zero-leakage nonvolatile FPGA (nvFPGA) promising. This paper proposes a hardware/ software co-design based nvFPGA with efficient checkpointing strategy. With nonvolatile checkpointing BRAM (CBRAM), it maintains both computation data as well as configuration when power-off to avoid expensive rollbacks due to data loss. A checkpointing location-aware technique is used to balance computation rollback overheads and backup energy. Experimental results show that the proposed checkpointing strategy can reduce 45.8% backup data of nvFPGA when system-level power gating happens.

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%.

Reliable and ultra-low power time synchronization becomes more and more important with the popularity of energy harvesting sensor nodes. This paper proposes an untethered and probabilistic ultra-lower power time synchronization method for energy intermittent sensor network. It avoids the frequent RF communications with the assistance of a solar clock. The SATS system consists of two main parts: the synchronizer, a low power solar clock module for time synchronization, and the S3-Mapping, an offline sequence matching algorithm. Furthermore, we develop an improved version of S3-Mapping, which reduces the computation complexity from exponential to linear using the redundancy models and the onion peeling method. The SATS system is validated by both simulations and a prototype, which shows that the second level synchronization precision can be achieved under reasonable probability. What's more, the energy consumption of time synchronization is reduced by over 1 ~ 2 magnitudes compared with the up-to-date low power time synchronization protocol.

In the near future, self-powered wearable sensor nodes are becoming as an emerging trend in healthcare applications. However, several design challenges exist before their wide usage, such as the limited and unstable power output as well as unpredictable power profiles. This paper shows an overview of candidate solutions and proposes that a cross-layer approach is needed.

Energy harvesting is gaining more and more attentions due to its characteristics of ultra-long operation time without maintenance. However, frequent unpredictable power failures from energy harvesters bring performance and reliability challenges to traditional processors. Nonvolatile processors are promising to solve such a problem due to their advantage of zero leakage and efficient backup and restore operations. To optimize the nonvolatile processor design, this paper proposes new metrics of nonvolatile processors to consider energy harvesting factors for the first time. Furthermore, we explore the nonvolatile processor design from circuit to system level. A prototype of energy harvesting nonvolatile processor is set up and experimental results show that the proposed performance metric meets the measured results by less than 6.27% average errors. Finally, the energy consumption of nonvolatile processor is analyzed under different benchmarks.