arXiv subjects

Di Mou

Publications and source records attributed to Di Mou.

6 recordsLinked to original sources

arXiv2026

Dissecting Outlier Dynamics in LLM NVFP4 Pretraining

Training large language models using 4-bit arithmetic enhances throughput and memory efficiency. Yet, the limited dynamic range of FP4 increases sensitivity to outliers. While NVFP4 mitigates quantization error via hierarchical microscaling, a persistent loss gap remains compared to BF16. This study conducts a longitudinal analysis of outlier dynamics across architecture during NVFP4 pretraining, focusing on where they localize, why they occur, and how they evolve temporally. We find that, compared with Softmax Attention (SA), Linear Attention (LA) reduces per-tensor heavy tails but still exhibits persistent block-level spikes under block quantization. Our analysis attributes outliers to specific architectural components: Softmax in SA, gating in LA, and SwiGLU in FFN, with "post-QK" operations exhibiting higher sensitivity to quantization. Notably, outliers evolve from transient spikes early in training to a small set of persistent hot channels (i.e., channels with persistently large magnitudes) in later stages. Based on these findings, we introduce Hot-Channel Patch (HCP), an online compensation mechanism that identifies hot channels and reinjects residuals using hardware-efficient kernels. We then develop CHON, an NVFP4 training recipe integrating HCP with post-QK operation protection. On GLA-1.3B model trained for 60B tokens, CHON reduces the loss gap to BF16 from 0.94% to 0.58% while maintaining downstream accuracy.

cs.LG↗

arXiv2025

Online Full ZVS Optimization for Modular Multi-Active Bridge Converter in MV PET

Multi-active bridge (MAB) converters, the core of the state-of-the-art medium-voltage power electronic transformers, can flexibly connect multiple DC ports among distributed DC grids and loads, but suffer from hard switching under conventional single phase-shift control, especially under unbalanced voltage conversion ratios and light load conditions. Although some offline methods manage to improve the efficiency through complex optimization structures, there lacks online optimization methods that are simple but effective due to the strong coupling among ports of the converter. By leveraging the time-domain model of the MAB converter under the multiple phase-shift modulation scheme, this paper simplifies the optimization process and proposes an online optimization method that can achieve full zero-voltage switching (ZVS) operation regardless of the load conditions. The proposed method has simple solutions with only voltage conversion ratios involved and can be implemented within a wide operation range without additional sensors or advanced controllers. A four-port MAB converter is constructed as the prototype. The simulation and experimental results have verified the feasibility and superiority of the proposed online strategy in achieving ZVS operation, dynamic response, and efficiency improvement.

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arXiv2025

DT-MPC: Synthesizing Derivation-Free Model Predictive Control from Power Converter Netlists via Physics-Informed Neural Digital Twins

Model Predictive Control (MPC) is a powerful control strategy for power electronics, but it highly relies on manually-derived and topology-specific analytical models, which is labor-intensive and time-consuming in practical designs. To overcome this bottleneck, this paper introduces a Digital-Twin-based MPC (DT-MPC) framework for generic power converters that can systematically translate a high-level circuit into an objective-aware control policy by leveraging a DT as a high-fidelity system model. Furthermore, a physics-informed neural surrogate predictor is proposed to accelerate predictions by DT and enable real-time operation. A gradient-free simplex search optimizer is also introduced to efficiently handle complex multi-objective optimization. The efficacy of the framework has been validated through a cloud-to-edge deployment on a 1500 W dual active bridge converter. Experimental results show that the synthesized predictive model achieves an inference speed over 7 times faster than real time, the DT-MPC controller outperforms several human-designed counterparts, and the overall framework reduces engineering design time by over 95\%, verifying the superiority of DT-MPC on generalized power converters.

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arXiv2025

Physics-Embedded Neural ODEs for Sim2Real Edge Digital Twins of Hybrid Power Electronics Systems

Edge Digital Twins (EDTs) are crucial for monitoring and control of Power Electronics Systems (PES). However, existing modeling approaches struggle to consistently capture continuously evolving hybrid dynamics that are inherent in PES, degrading Sim-to-Real generalization on resource-constrained edge devices. To address these challenges, this paper proposes a Physics-Embedded Neural ODEs (PENODE) that (i) embeds the hybrid operating mechanism as an event automaton to explicitly govern discrete switching and (ii) injects known governing ODE components directly into the neural parameterization of unmodeled dynamics. This unified design yields a differentiable end-to-end trainable architecture that preserves physical interpretability while reducing redundancy, and it supports a cloud-to-edge toolchain for efficient FPGA deployment. Experimental results demonstrate that PENODE achieves significantly higher accuracy in benchmarks in white-box, gray-box, and black-box scenarios, with a 75% reduction in neuron count, validating that the proposed PENODE maintains physical interpretability, efficient edge deployment, and real-time control enhancement.

cs.LG↗

arXiv2025

Neural Substitute Solver for Efficient Edge Inference of Power Electronic Hybrid Dynamics

Advancing the dynamics inference of power electronic systems (PES) to the real-time edge-side holds transform-ative potential for testing, control, and monitoring. How-ever, efficiently inferring the inherent hybrid continu-ous-discrete dynamics on resource-constrained edge hardware remains a significant challenge. This letter pro-poses a neural substitute solver (NSS) approach, which is a neural-network-based framework aimed at rapid accurate inference with significantly reduced computational costs. Specifically, NSS leverages lightweight neural networks to substitute time-consuming matrix operation and high-order numerical integration steps in traditional solvers, which transforms sequential bottlenecks into highly parallel operation suitable for edge hardware. Experimental vali-dation on a multi-stage DC-DC converter demonstrates that NSS achieves 23x speedup and 60% hardware resource reduction compared to traditional solvers, paving the way for deploying edge inference of high-fidelity PES dynamics.

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arXiv2023

An Event-Based Synchronization Framework for Controller Hardware-in-the-loop Simulation of Electric Railway Power Electronics Systems

The Controller Hardware_in_the_loop (CHIL) simulation is gaining popularity as a cost_effective, efficient, and reliable tool in the design and development process of fast_growing electrified transportation power converters. However, it is challenging to implement the conventional CHIL simulations on the railway power converters with complex topologies and high switching frequencies due to strict real_time constraints. Therefore, this paper proposes an event-based synchronization CHIL (ES_CHIL) framework for high_fidelity simulation of these electrified railway power converters. Different from conventional CHIL simulations synchronized through the time axis, the ES_CHIL framework is synchronized through the event axis. Therefore, it can ease the real_time constraint and broaden the upper bound on the system size and switching frequency. Besides, models and algorithms with higher accuracy, such as the diode model with natural commutation processes, can be used in the ES-CHIL framework. The proposed framework is validated for a 350 kW wireless power transformer system containing 24 fully controlled devices and 36 diodes by comparing it with Simulink and physical experiments. This research improves the fidelity and application range of the power converters CHIL simulation. Thus, it helps to accelerate the prototype design and performance evaluation process for electrified railways and other applications with such complex converters.

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