Author

Chang-Xu Liu

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#edge computing Open access Sep 2026

SATLLM: A Sparsity-aware Accelerator for Ternary-Weight Large Language Models

Large language models (LLMs) exhibit strong performance across applications, but their inference is computationally intensive, posing significant challenges for edge deployment. Quantization is among the most effective and widely used optimizations. In particular, ternary-weight quantization further lowers compute cost...

Chang-Xu Liu, Yi-Fan Song, Yi-Feng Yang et al. · ACM Transactions on Design A... · 0 citations

#artificial intelligence Preprint Sep 2026

Adapting Tree-Structured Speculative Decoding to DeepSeek-V4 for Efficient Inference

Repeated execution of the target model during autoregressive decoding is a major source of LLM inference latency. Unlike linear speculation, which follows a single candidate chain, tree-structured speculation retains multiple branches from shared prefixes; under the same budget, this broader coverage can improve accept...

Chang-Xu Liu, Zhao-Geng Li · 0 citations