13 papers
SelecTKD: Selective Token-Weighted Knowledge Distillation for LLMs
Haiduo Huang, Jiangcheng Song, Yadong Zhang +1
Knowledge distillation (KD) is a standard route to compress Large Language Models (LLMs) into compact students, yet most pipelines uniformly apply token-wise loss regardless of tea…
FastEagle: Cascaded Drafting for Accelerating Speculative Decoding
Haiduo Huang, Jiangcheng Song, Wenzhe Zhao +1
Speculative decoding accelerates generation by drafting candidates and verifying them in parallel, yet state-of-the-art drafters (e.g., EAGLE) still require N sequential passes to…
SpecVLM: Fast Speculative Decoding in Vision-Language Models
Haiduo Huang, Fuwei Yang, Zhenhua Liu +4
Speculative decoding is a powerful way to accelerate autoregressive large language models (LLMs), but directly porting it to vision-language models (VLMs) faces unique systems cons…
IsoSched: Preemptive Tile Cascaded Scheduling of Multi-DNN via Subgraph Isomorphism
Boran Zhao, Zihang Yuan, Yanbin Hu +5
Deploying deep neural network (DNN) accelerators with Layer Temporal Scheduling (LTS) often incurs significant overheads (e.g., energy and latency), as intermediate activations mus…
AdapSNE: Adaptive Fireworks-Optimized and Entropy-Guided Dataset Sampling for Edge DNN Training
Boran Zhao, Hetian Liu, Zihang Yuan +5
Training deep neural networks (DNNs) directly on edge devices has attracted increasing attention, as it offers promising solutions to challenges such as domain adaptation and priva…
SparseMap: A Sparse Tensor Accelerator Framework Based on Evolution Strategy
Boran Zhao, Haiming Zhai, Zihang Yuan +4
The growing demand for sparse tensor algebra (SpTA) in machine learning and big data has driven the development of various sparse tensor accelerators. However, most existing manual…