With the rapid expansion of the Internet of Things (IoT) and the increasing unmanned devices, data transmission and sharing between unmanned agents in the Internet of Unmanned Agents (IUA) face significant challenges. Mobile Edge Computing (MEC), which extends computing power from the cloud to the network edge, has become a key technology for enabling efficient, low-latency communication services. However, with the surge in the number of terminal devices and the diversification of service requirements, traditional MEC deployment methods face challenges such as inflexible resource allocation and limited service coverage. This paper mainly researches an unmanned aerial vehicle(UAV)-assisted ground-to-air communication and computing system, constructs a multi-UAV network communication model and a computing model, and proposes a joint optimization problem of system task processing delay and energy consumption based on the total system delay and the energy consumption of the UAV. For the path planning and resource allocation problems of the multi-UAV network, an improved double-delay deep deterministic policy gradient algorithm is proposed to jointly optimize the trajectory planning, user association and task offloading strategies of the multi-agent UAV. Finally, the performance of the proposed algorithm is verified and analyzed through simulation experiments.

Virtual reality (VR) wearable devices can achieve immersive entertainment by fusing multi-modal tasks from various senses. However, constrained by the short battery life and limited hardware resources of the VR devices, running multiple tasks simultaneously with different modals is difficult. In this paper, we propose an energy-efficient accelerator that supports Multi-modal Tasks for VR devices, namely MTVR. We present a multi-task computing solution based on the flexible multi-task computing core design and efficient computing unit allocation strategy, which simultaneously achieves efficient work of multi-modal tasks. We design an early exit detector to skip invalid calculations, greatly saving energy. In addition, a fine-grained tiny value skip method at multiplier and adder levels is proposed to save energy further. We provide a hybrid RRAM and SRAM memory access scheme, reducing the external memory access (EMA). Through experimental evaluation, the multitask computing core achieves an average computational utilization of 95%. When the invalid input ratio is 90%, energy saving brought by the early exit detector can reach 88%. The tiny value skip method further achieved 13% energy saving. Hybrid memory access scheme obtains 98.9% EMA reduction. We deployed the MTVR accelerator in FPGA and self-designed RRAM, achieving energy efficiency of 3.6 TOPS/W, higher than other single-task accelerators.

Wearable devices for ECG arrhythmia detection based on artificial neural networks (ANN) are very popular. However, the energy consumption of electrocardiogram (ECG) processing in ANN has become one of the most critical factors. One solution is using a spiking neural network (SNN), effectively reducing power consumption and improving energy efficiency. Nevertheless, the inevitable membrane potential storage and accumulation of SNN result in significant energy and area overheads. This paper proposes a time domain (TD) based SNN processor for ECG classification. We propose a novel memory delay unit (MDU), part of the memory delay line (MDL), to store and accumulate membrane potential. With this method, power consumption can be significantly reduced. Also, we propose a wave generator that works with MDL to maximize computing efficiency. Compared with digital neurons, our proposed TD neurons reduce power consumption by 32.5% and achieve a classification accuracy of 96.8%. It is very suitable for arrhythmia detection wearable devices.

For the wearable biomedical signal detection, both high accuracy and low-power consumption are critical requirements. Various works have employed the neural network to improve the detecting accuracy and develop the biomedical processor. However, the biomedical processor with neural network engine contains massive data movements and large data buffers. One solution is the computing-in memory (CIM) architecture, which locates more data near the computing engine to reduce data movements. In traditional CIM-based solution, the detecting accuracy and power consumption is difficult to be optimized simultaneously, where the accuracy should be satisfied for the detection. To date, the time-domain computing engine have been developed to employ both digital and time domain computation. In this work, we present a high-precision time-domain engine to perform 8-bit multiplication and addition operation for the biomedical signal detection. With the high precision time-domain engine, we develop a CIM-based neural-network processor, namely TDPRO, to perform the detection of arrhythmia. In addition, we develop TD-zero-jumping (TDJ) and idle-shutdown (ISD) techniques according to signal features and data mapping strategy, further optimizing the power consumption. Based on our evaluation, the TD-based 8-bit mulitply-accumulation operation is robust, without declining the accuracy of biomedical signal detection. We design a ECG processor with the proposed TDPRO architecture, which obtains 98.60% high accuracy and 75.7% power saving compared to the recent the state-of-the-art study.

In this paper, the hierarchical control strategy of a photovoltaic/battery based dc microgrid is presented for electric vehicle (EV) wireless charging. Considering irradiance variations, battery charging/discharging requirements, wireless power transmission characteristics, and onboard battery charging power change and other factors, the possible operation states are obtained. A hierarchical control strategy is established, which includes central and local controllers. The central controller is responsible for the selection and transfer of operation states and the management of the local controllers. Local controllers implement these functions, which include PV maximum power point tracking (MPPT) algorithm, battery charging/discharging control, voltage control of DC bus for high-frequency inverter, and onboard battery charging control. By optimizing and matching parameters of transmitting coils, receiving coils and compensation capacitors, the wireless power transmission system is designed to be resonant when it is operating at the rated power, with the aim to achieve the optimum transmission system efficiency. Simulation and experimental results of the hierarchical control of the microgrid with electric vehicle wireless charging are established, showing the effectiveness of the proposed approach.