14 citations · 21 across the 9 of their papers we have counts for
6 papers · 1 filter
Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language Models
Song Guo, Jiahang Xu, Li Lyna Zhang +1
Despite the remarkable success of Large Language Models (LLMs), the massive size poses significant deployment challenges, particularly on resource-constrained hardware. While exist…
Constraint-aware and Ranking-distilled Token Pruning for Efficient Transformer Inference
Junyan Li, Li Lyna Zhang, Jiahang Xu +9
Deploying pre-trained transformer models like BERT on downstream tasks in resource-constrained scenarios is challenging due to their high inference cost, which grows rapidly with i…
Accurate and Structured Pruning for Efficient Automatic Speech Recognition
Huiqiang Jiang, Li Lyna Zhang, Yuang Li +7
Automatic Speech Recognition (ASR) has seen remarkable advancements with deep neural networks, such as Transformer and Conformer. However, these models typically have large model s…
ElasticViT: Conflict-aware Supernet Training for Deploying Fast Vision Transformer on Diverse Mobile Devices
Chen Tang, Li Lyna Zhang, Huiqiang Jiang +6
Neural Architecture Search (NAS) has shown promising performance in the automatic design of vision transformers (ViT) exceeding 1G FLOPs. However, designing lightweight and low-lat…
SpaceEvo: Hardware-Friendly Search Space Design for Efficient INT8 Inference
Li Lyna Zhang, Xudong Wang, Jiahang Xu +6
The combination of Neural Architecture Search (NAS) and quantization has proven successful in automatically designing low-FLOPs INT8 quantized neural networks (QNN). However, direc…
LUT-NN: Empower Efficient Neural Network Inference with Centroid Learning and Table Lookup
Xiaohu Tang, Yang Wang, Ting Cao +5
On-device Deep Neural Network (DNN) inference consumes significant computing resources and development efforts. To alleviate that, we propose LUT-NN, the first system to empower in…