3 papers
cs.CL2026
NeuronMLP: Efficient LLM Inference via Singular Value Decomposition Compression and Tiling on AWS Trainium
Dinghong Song, Jierui Xu, Weichu Yang +2
Emerging AI accelerators have started to gain attention and offer new opportunities for efficient inference of large language models (LLMs). Trainium, an AI accelerator recently de…
cs.CR2026
Adversarial Contrastive Learning for LLM Quantization Attacks
Dinghong Song, Zhiwei Xu, Hai Wan +3
Model quantization is critical for deploying large language models (LLMs) on resource-constrained hardware, yet recent work has revealed severe security risks that benign LLMs in f…
cs.CL2025
AttnCache: Accelerating Self-Attention Inference for LLM Prefill via Attention Cache
Dinghong Song, Yuan Feng, Yiwei Wang +6
Large Language Models (LLMs) are widely used in generative applications such as chatting, code generation, and reasoning. However, many realworld workloads such as classification,…