Papers by Aimin Pan
Deputy: Accelerating Large Language Model Inference with Dynamic Low-Rank Substitution (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing dynamic schemes such as early-exit and layer-drop reduce FLOPs but break batch processing or introduce KV-cache inconsistency. |
| Approach: | They propose a dynamic low-rank substitution framework that employs a lightweight decision module at each layer to dynamically determine the execution branch for different tokens. |
| Outcome: | The proposed model reduces computation by approximately 40% compared to the original dense model while outperforming existing baseline methods. |
FlattenQuant: Breaking through the Inference Compute-bound for Large Language Models with Per-tensor Quantization (2024.lrec-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated state-of-the-art accuracies across tasks, but their latency and GPU memory consumption limit their performance. |
| Approach: | They propose a method which flattens the tensor to achieve low bit per-tensori quantization with minimal accuracy loss. |
| Outcome: | The proposed method achieves low bit per-tensor quantization with minimal accuracy loss. |