Skip to content
Log in

Author

Jia-Ning Chen

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

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 conditional computation and effectively reduces the computational complexity of large language models, providing a valuable opportunity for designing an efficient hardware accelerator. However, when applied to vision transformers, MoD suffers from accuracy degradation and excessive external memory access (EMA). Therefore, this article presents ME-MoD, the first memory-efficient MoD-based vision transformer inference accelerator, leveraging the idea of reordering and algorithm-hardware-dataflow codesign. Algorithmically, distribution adjustment forward (DAF) and routing decision forward (RDF) techniques restore accuracy and alleviate memory access costs through token reordering. Architecturally, a LayerNorm-Routing (L-R) fusion module and a token reordering and sequential recording module enhance computational efficiency while minimizing memory overhead. In addition, a token-stationary layer fusion dataflow and an on-chip dynamic memory module are designed, which further optimizes the EMA caused by the intermediate results of interlayer computation of valid tokens routed by MoD. With negligible accuracy loss, our ME-MoD accelerator achieves $1.62\times $ inference speed up, eliminates 46.5% of the external memory bandwidth requirement and 45.2% of energy consumption compared with standard MoD. It achieves 23.6 TOPS/W energy efficiency, which is $4.02\times $ improvements compared with state-of-the-art (SOTA) designs.

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

Short-term load forecasting (STLF) provides essential information for numerous applications in modern power systems. However, accurate STLF often relies on fine-grained smart-meter data from distributed users, raising increasing concerns about data privacy. Federated learning (FL) has therefore emerged as a promising privacy-preserving paradigm for STLF. Nevertheless, this paper reveals structured heterogeneity in clients'load data. Specifically, clients exhibit different responses to exogenous factors and distinct temporal load profiles, which can degrade forecasting performance in FL. To mitigate these issues, this paper studies the role of model initialization in federated STLF, and proposes two initialization strategies from global and local perspectives. For global model initialization, when auxiliary public load data are available, a pretrained initialization strategy is developed to initialize the global model before federated training, thereby reducing client drift during the training process. For local model initialization, we propose SLIAvg, a sequential local initialization strategy that promotes a more consistent training process by allowing participating clients to start from progressively adapted models within each communication round. Since the proposed strategies only modify the initialization process, they are compatible with most existing FL frameworks and privacy-enhancing techniques. Experiments on real smart-meter data with two representative forecasting architectures demonstrate that the proposed strategies 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