Wireless Sensor Network (WSN) reliability evaluation is essential for ensuring the stable operation of network. Traditional methods usually focus on the network topology structure, and calculate the normal operation probability of WSNs. However, these methods usually ignore the energy consumption and network lifetime. In this paper, a novel reliability evaluation algorithm TLR is proposed, which calculates the network lifetime under dynamic network environment according to the pre-set network topology structure reliability threshold, and realizes the comprehensive reliability analysis of network topology and lifetime. In addition, as the basis for reliability evaluation, this paper proposes a new deep reinforcement learning network framework GNN-AC combining graph neural network and actor-critic network, which solves the challenge of constructing Virtual Backbone Network (VBN) in dynamically operating networks. Based on the self-defined fitness matrix and fitness value, the objective function is set to optimize the VBN construction scheme to accurately calculate the network lifetime, and the relationship between the reliability of network topology and network lifetime is discussed. Simulations are carried out for various sizes of WSNs to show the advantages and effectiveness of the proposed approach in estimating network lifetime and reliability evaluation.

Zero-knowledge proof (ZKP) is a promising cryptographic protocol, but its practical deployment is hindered by the time-consuming proof generation. The proof generation inherently exhibits high-degree parallelism, yet challenges persist in exploiting fine-grained parallelism due to the dataflow complexity, impeding previous work to achieve optimal acceleration. In this work, we propose FZKP, a ZKP accelerator that utilizes two novel fine-grained dataflows coupled with two forward-flow microarchitectures to alleviate dataflow complexity, efficiently exploiting fine-grained parallelism. The proposed dataflows simplify the dataflow pattern for parallel execution, disclosing fine-grained parallelism at a low cost. The microarchitectures employ a base design to handle large bit-width intermediate results for timely consumption. They then replicate and combine the base design following the proposed dataflow to facilitate parallel execution. When evaluated in 12 nm, FZKP achieves an average speedup of 10.3× and 2.2× over the state-of-the-art GPU-based solution and ZKP accelerator on real-world workloads, respectively.

Cross-workload design space exploration (DSE) is crucial in CPU architecture design. Existing DSE methods typically employ the transfer learning technique to leverage knowledge from source workloads, aiming to minimize the requirement of target workload simulation. However, these methods struggle with overfitting, data ambiguity, and workload dissimilarity. To address these challenges, we reframe the cross-workload CPU DSE task as a few-shot meta-learning problem and further introduce MetaDSE. By leveraging model agnostic meta-learning, MetaDSE swiftly adapts to new target workloads, greatly enhancing the efficiency of cross-workload CPU DSE. Additionally, MetaDSE introduces a novel knowledge transfer method called the workload-adaptive architectural mask algorithm, which uncovers the inherent properties of the architecture. Experiments on SPEC CPU 2017 demonstrate that MetaDSE significantly reduces prediction error by 44.3% compared to the state-of-theart. MetaDSE is open-sourced and available at this anonymous GitHub.

A physics-informed deep transfer reinforcement learning (PIDTRL) strategy is proposed for enabling advanced control and modulation of a dc solid state transformer (DCSST) in a dc microgrid. The strategy involves three stages: (i) Centralized training of deep reinforcement learning agents to balance power and reduce current stress in the DCSST; (ii) effective knowledge transfer from a Source-simulation system to a Target-experimental system using minimal experimental data; and (iii) deployment of multiple agents for online control in the DCSST. The proposed method adaptively determines optimal modulation variables (duty cycles and phase shifts) in stochastic and uncertain environments without requiring accurate model information. Experimental results validate the effectiveness of the proposed PIDTRL algorithm.

The space-terrestrial integrated network (STIN) is one of the key development directions for future network architectures, offering global seamless connectivity, efficient resource utilization, and enhanced service reliability. Recently, the generalized filter bank orthogonal frequency division multiplexing (GFB-OFDM) has been proposed for the STIN system, which enables a flexible waveform mechanism for both terrestrial and non-terrestrial systems. However, research on GFB-OFDM detector remains scarce. In this paper, we model the symbol detection problem and introduce the linear detector and the non-linear detector into the GFB-OFDM system. Furthermore, based on the traditional expectation propagation (EP) algorithm, we propose a low-complexity eigenvalue approximation-based EP detector for the GFB-OFDM system, which achieves a lower complexity compared with the existing EP-like detector. Finally, the better error performance and the lower computational complexity of the proposed receiver are validated by the numerical results.

Heterogeneous graph neural networks (HGNNs) have expanded graph representation learning to heterogeneous graph fields. Recent studies have demonstrated their superior performance across various applications, including circuit representation, chip design automation, and placement optimization, often surpassing existing methods. However, GPUs often experience inefficiencies when executing HGNNs due to their unique and complex execution patterns. Compared to traditional graph neural networks (GNNs), these patterns further exacerbate irregularities in memory access. To tackle these challenges, recent studies have focused on developing domain-specific accelerators for HGNNs. Nonetheless, most of these efforts have concentrated on optimizing the datapath or scheduling data accesses, while largely overlooking the potential benefits that could be gained from leveraging the inherent properties of the semantic graph, such as its topology, layout, and generation. In this work, we focus on leveraging the properties of semantic graphs to enhance HGNN performance. First, we analyze the semantic graph build (SGB) stage and identify significant opportunities for data reuse during semantic graph generation. Next, we uncover the phenomenon of buffer thrashing during the graph feature processing (GFP) stage, revealing potential optimization opportunities in semantic graph layout. Furthermore, we propose a lightweight hardware accelerator frontend for HGNNs, called SiHGNN. This accelerator frontend incorporates a tree-based SGB for efficient semantic graph generation and features a novel Graph Restructurer for optimizing semantic graph layouts. Experimental results show that SiHGNN enables the state-of-the-art HGNN accelerator to achieve an average performance improvement of$2.95\times $.

Internet of Things (IoT) reliability evaluation contributes to the sustainable computing and enhanced stability of the network. Previous algorithms usually evaluate the reliability of IoT by enumenating the states of nodes and networks, which are difficult to handle IoT with hundreds of nodes because the computational cost. In this paper, a novel algorithm, TMCRA, is proposed to evaluate the reliability of IoT in complex network environment, which consider both coverage and connectivity. For coverage, TMCRA employs the Confident Information Coverage (CIC) model to divide the target area into independent grids and calculates the coverage rate. In terms of connectivity, TMCRA forming the Virtual Backbone Network (VBN) based on two proposed methods: TMA and MGIN, and evaluate connectivity by analyzing the VBN rather than the whole network. The TMA and MGIN are two algorithms for constructing Minimum Connected Dominant Sets (MCDS), which are suitable for different scale networks. Finally, based on the data of coverage and connectivity, TMCRA utilizes tensors for the unified modeling and representation of network structure, and calculates IoT reliability based on the tensors. Simulations are carried out for various sizes of IoT to show the advantages and effectiveness of the proposed approach in reliability evaluation.

This paper introduces a novel analytical model for the accurate inductance calculation of rounded rectangular coils in wireless power transfer systems (WPTSs). Comprehensive analysis is conducted for the rounded rectangular coil and a new parametric model is proposed to precisely define its structure. A new inductance calculation model is proposed for the rounded rectangular coils based on Neumann's formula and the obtained parametric structure model. The Geometric Mean Distance method is introduced to reduce the complexity and difficulty of self-inductance modeling. The image current method is adopted to achieve accurate inductance modeling for rounded rectangular coils with ferrite plates. Finally, the accuracy of the proposed analytical model is validated and compared with two other traditional methods. It turns out that the proposed models can achieve high calculation accuracy in self-inductance and mutual inductance calculation. The proposed model exhibits the merits of high accuracy and fast calculation speed which makes it promising for future coil designing and optimization.

Despite the widespread use of resonant converters in various applications, their analysis and design still necessitate a solid comprehension of the converter's operating principle and nonlinear behavior, which can be a significant manpower burden. To alleviate this issue and free us from repetitive work, this paper proposes a zero-knowledge artificial neural network (ZANN)-based waveform and critical parameter calculator for resonant converters. A normalized resonant network is employed to generate adequate data for training the ZANN. The ZANN can identify the essential characteristics of different resonant networks based on the configuration of hyperparameters. The accuracy of the calculator is verified using a 3-kW 480-V CLLC prototype. The results show that the accuracy of the root-mean-square (RMS) values is less than 5%, while the accuracy for the peak values is less than 20%.

The CLLC resonant converters are widely used in data center, battery chargers, electrical vehicles due to its high efficiency and high power density. The voltage gain characteristic of CLLC resonant converters plays an important role in both operation analysis and design optimization. This paper presents a precise and fast backpropagation neural network (BPNN)-based voltage gain model of CLLC that considers two degrees of freedom (2DOFs), the switching frequency$\boldsymbol{f}_{\mathbf{s}}$and duty ratio$\boldsymbol{d}$, in all operation conditions. The effectiveness of the model is verified by a 3-kW 480-V CLLC prototype. The voltage gain error is reduced by 95% compared to the conventional fundamental harmonic approximation (FHA) method, and the calculation burden is reduced by 99% compared to the time-domain method.