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lssue 5, 2026
Editors-in-Chief
Yunhe Pan, Aiguo Fei
Advisor Xicheng Lu
ISSN 3069-8928
CN 33-1439/TP
IF (2024 JCR) 2.9
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Volume 27 Issue 5，2026
Regular Papers
From software-defined interconnect to software-defined system-on-wafer: a computing architecture revolution in the post-Moore era AI Introduction
Abstract：As Moore’s law approaches its fundamental physical and economic limits, the semiconductor industry faces unprecedented challenges in maintaining performance growth. This study presents the revolutionary evolution from software-defined interconnect (SDI) to software-defined system-on-wafer (SDSoW), a paradigm-shifting architectural approach that transcends traditional scaling constraints through wafer-level heterogeneous integration. Our proposed SDSoW enables dynamic reconfiguration of thousands of computing chiplets across an entire wafer, achieving superlinear performance scaling and significantly improving energy efficiency. We establish a comprehensive theoretical framework with mathematical models covering key aspects, such as interconnect flexibility and integration scaling, and propose an application-driven dynamic architecture reconfiguration (ADR) paradigm that optimizes wafer-scale resources in real time and may foster emergent intelligence in large, heterogeneous systems. Simulation results (128–1024 nodes) demonstrate that SDSoW outperforms conventional multi-chip systems, delivering approximately 3.73×–4.39× higher throughput, 79.2% lower latency, and 2.8 × higher power efficiency. As a paradigm shift comparable to the invention of integrated circuits (ICs), it provides a viable pathway beyond Moore’s law through innovative architectural design rather than process scaling.Keywords：Software-defined interconnect (SDI);Software-defined system-on-wafer (SDSoW);Wafer-level integration;Emergent intelligence;Heterogeneous computing5|0|0Updated：2026-05-27Regular Papers
High-precision temperature prediction for atmospheric refractivity correction using Kalman spatiotemporal data fusion Enhanced Publication AI Introduction
Abstract：In absolute distance measurement and positioning applications, atmospheric refraction error is a critical factor limiting measurement accuracy. Temperature plays a dominant role in computing the atmospheric refractive index. However, accurately acquiring the temperature field along the ranging path in complex and dynamic outdoor environments remains challenging due to limited sensor deployment and environmental nonstationarity. We propose a spatiotemporal temperature data fusion method for atmospheric refraction correction, which integrates the strengths of the generalized regression neural network (GRNN) and Kriging interpolation within a Kalman filter. This method achieves dynamic prediction and high-accuracy reconstruction of temperature parameters. The proposed method is systematically validated through simulation analysis as well as indoor and kilometer-scale outdoor experimental measurements. The simulation results demonstrate that Kalman filter expanded fusion (KFEF) outperforms the traditional interpolation method radial basis function (RBF) and the state-of-the-art spatiotemporal interpolation and prediction methods spatiotemporal Kriging (STK) and Gaussian process (GP), in terms of both reconstruction accuracy and stability of the temperature field. Specifically, KFEF achieves a 61.54% reduction in root mean square error (RMSE) compared with RBF and reductions of 34.21% and 32.43% relative to STK and GP, respectively. This indicates its practical value for long-distance high-precision ranging engineering applications. Furthermore, the proposed spatiotemporal data fusion framework is highly general and scalable. It can also be applied to other temperature field prediction and reconstruction problems.Keywords：Temperature prediction;Kalman filter expanded fusion (KFEF);Atmospheric refraction correction;Absolute distance measurement;Generalized regression neural network (GRNN) optimization4|0|0Updated：2026-05-27Regular Papers
MH-Raft: an efficient and low-latency consensus algorithm for distributed systems via MOEA/D and hybrid hierarchical clustering Enhanced Publication AI Introduction
Abstract：Raft is a foundational consensus protocol for distributed systems, architected to ensure state machine replication and data consistency across machine clusters. However, traditional Raft faces significant performance bottlenecks, particularly regarding suboptimal election efficiency and substantial consensus latency in large-scale deployments. To address these challenges, this study presents MH-Raft, an enhanced consensus variant designed for high efficiency and minimal latency. We propose a hierarchical node management and election framework to optimize network coordination. Specifically, a leader election methodology leveraging the multi-objective evolutionary algorithm based on decomposition (MOEA/D) is formulated to minimize election latency by evaluating multi-dimensional node attributes. To further refine the proposed hierarchical architecture, a rigorous tightness definition is devised for optimal mediator node selection, which is integrated into a hybrid clustering algorithm that adaptively partitions the network and optimizes the mapping between mediator nodes and follower nodes. Quantitative evaluations via comprehensive experiments demonstrate that MH-Raft significantly reduces overall election latency and lowers consensus latency by 14.87%–34.45%, while enhancing average throughput by 30.43% compared to the conventional Raft implementation.Keywords：Consensus algorithm;Blockchain;Multi-objective evolutionary algorithm;Distributed systems2|0|0Updated：2026-05-27Regular Papers
An approach to characterizing the power system security region by integrating distributionally robust optimization and Transformer-based deep learning Enhanced Publication AI Introduction
Abstract：Renewable generation and load uncertainty pose significant challenges to power system security, necessitating efficient approaches to characterizing high-dimensional security regions. To overcome the curse of dimensionality, uncertainty neglect, and undue conservatism in existing methods, this paper proposes an approach integrating distributionally robust optimization (DRO) and deep learning for security region characterization. First, to properly account for uncertainty while avoiding excessive conservatism, a DRO-based active search strategy is developed to identify critical boundary points, where diffusion-generated renewable scenarios and load-deviation samples constructed around typical demand profiles are jointly used to build a robust probabilistic ambiguity set. Subsequently, a Transformer-based model learns from these boundary points to reconstruct the full high-dimensional security region. The model’s self-attention mechanism captures the global nonlinear dependencies among dimensions, enabling a precise and efficient boundary fit. Simulations on IEEE test systems confirm that the approach accurately characterizes high-dimensional security regions at a low computational cost, yielding a security region with strong robustness to renewable-load uncertainty. This work offers a new paradigm for security assessment and decision support in power systems under high uncertainty.Keywords：Security region;Distributionally robust optimization;Deep learning;Transformer model;Data-driven3|0|0Updated：2026-05-27Regular Papers
HyRAS: a hybrid redundancy- and serialization-based fault-tolerant architecture for through-silicon vias AI Introduction
Abstract：Three-dimensional network-on-chips (3D NoCs) are increasingly used to improve scalability in multicore systems. Through-silicon via (TSV) is a critical technology for enabling vertical interconnects between NoC layers. However, TSV-based interlayer connections are highly prone to faults resulting from manufacturing defects, aging, or other sources, which compromise system reliability. To address these challenges, particularly in chiplet-based 3D NoCs, robust fault-tolerant mechanisms are crucial for maintaining operational integrity in the presence of TSV faults. We introduce a novel fault-tolerant architecture designed to ensure persistent communication reliability despite permanent vertical link failures, named HyRAS, a hybrid redundancy- and serialization-based method. Our approach is built on two synergistic mechanisms. First, a lightweight spatial redundancy-based scheme leverages shared TSV resources to mitigate the impact of isolated faults. Second, for more severe fault scenarios, an adaptive serialization-based strategy is employed to maintain connectivity by efficiently using the remaining functional links. The architecture is rigorously evaluated through functional simulations using both synthetic traffic patterns and realistic application workloads. Compared to contemporary fault-tolerant methods, HyRAS achieves up to 28.2% higher throughput under realistic workloads with significant defect clusters. These gains are achieved with only modest overhead, incurring a 14.53% increase in area and 8.87% increase in power consumption relative to the standard redundancy-based router.Keywords：Three-dimensional network-on-chip (3D NoC);Through-silicon vias (TSVs);Redundancy;Fault-tolerant2|0|0Updated：2026-05-27Regular Papers
Abstract：Accurate assessment of human exposure to millimeter-wave (mmWave) electric fields (E-fields) has recently become critical for public health and safety. High-spatial-resolution E-field distribution is required for assessment of mmWave electromagnetic exposure according to the International Electrotechnical Commission (IEC) and the Institute of Electrical and Electronics Engineers (IEEE) (IEC/IEEE 63195-2 standard). This study proposes a generative adversarial network (GAN) integrated with field gradient loss, termed EFGraGAN, for superresolution reconstruction of mmWave E-fields. The incorporation of E-field gradient loss enables the network to learn both local field magnitudes and spatial structures, thereby enhancing the accuracy and fine structural details of reconstructed E-field maps. To improve generalization across antenna types, the training dataset is generated using plane-wave integral representation (PWIR) and randomized parametric incidence, simulating diverse field distributions. Combined with bilinear interpolation, the method achieves high-resolution reconstruction at 30 GHz and 60 GHz, meeting the requirements of the IEC/IEEE 63195-2 standard for exposure assessment. Numerical simulations show that EFGraGAN reconstructs E-field distributions in a skin phantom with a maximum mean relative error (MRE) of <9% up to 60 GHz in a 4×4 dipole array scenario, outperforming conventional interpolation and traditional GAN methods. The approach also demonstrates strong robustness to noise, enabling current measurement systems to achieve accurate and efficient evaluation of mmWave exposure.Keywords：Field reconstruction;Generative adversarial network (GAN);Millimeter-wave (mmWave) exposure6|0|0Updated：2026-05-27
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