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Author

Guolin Tang

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Book Open access Sep 2026

SSQT: A Hardware-Friendly Fusion Compression Framework of Structured Sparsification and Sensitivity-Driven Quantization for Large-Scale Language Models

The results show that compression and execution layout must be co-designed to convert model-size reduction into practical parallel inference acceleration, and that compression and execution layout must be co-designed to convert model-size reduction into practical parallel inference acceleration.

Qian-Sheng Song, Guo-Lin Tang · 0 citations
#large language models Book Open access Sep 2026

SSQT: A Hardware-Friendly Fusion Compression Framework of Structured Sparsification and Sensitivity-Driven Quantization for Large-Scale Language Models

Large language models (LLMs) are often memory-bandwidth bound during autoregressive decoding, so reducing weight storage does not automatically produce parallel speedup when the compressed representation is irregular. We present SSQT, a post-training framework that jointly applies hardware-aligned structured sparsifica...

Qian-Sheng Song, Guolin Tang · 0 citations

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