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Jia-Ning Chen

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Sep 2026

ME-MoD: Algorithm-Hardware-Dataflow Co-Design for Memory Efficient Mixture-of-Depth-Based Vision Transformer Accelerator

Transformers outperform traditional neural networks but face high computational and memory costs, limiting edge device deployment. Although many hardware accelerators aim to address this, the original Transformer structure still restricts the optimization effect. A recent breakthrough, mixture-of-depths (MoDs), employs...

Jia-Ning Chen, Wen-Long Ma, Yun-Chuan Li et al. · 0 citations
#machine learning Preprint Aug 2026

Initialization Is Critical: Advancing Federated Short-Term Load Forecasting under Load Heterogeneity via Model Initialization

This paper studies the role of model initialization in federated STLF, and proposes two initialization strategies from global and local perspectives, which effectively improve forecasting performance, as evidenced by reduced client drift, improved convergence behavior, and lower forecasting errors.

Jia-Ning Chen, Vajiheh Farhadi, Yan Li et al. · 0 citations

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