3.5k citations · 5.9k across the 14 of their papers we have counts for
14 papers
QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs
Wei Huang, Yi Ge, Shuai Yang +11
We propose QeRL, a Quantization-enhanced Reinforcement Learning framework for large language models (LLMs). While RL is essential for LLMs' reasoning capabilities, it is resource-i…
VILA: On Pre-training for Visual Language Models
Ji Lin, Hongxu Yin, Wei Ping +7
Visual language models (VLMs) rapidly progressed with the recent success of large language models. There have been growing efforts on visual instruction tuning to extend the LLM wi…
Retrospective: EIE: Efficient Inference Engine on Sparse and Compressed Neural Network
Song Han, Xingyu Liu, Huizi Mao +4
EIE proposed to accelerate pruned and compressed neural networks, exploiting weight sparsity, activation sparsity, and 4-bit weight-sharing in neural network accelerators. Since pu…
BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation
Zhijian Liu, Haotian Tang, Alexander Amini +4
Multi-sensor fusion is essential for an accurate and reliable autonomous driving system. Recent approaches are based on point-level fusion: augmenting the LiDAR point cloud with ca…
PatchNet -- Short-range Template Matching for Efficient Video Processing
Huizi Mao, Sibo Zhu, Song Han +1
Object recognition is a fundamental problem in many video processing tasks, accurately locating seen objects at low computation cost paves the way for on-device video recognition.…
A Delay Metric for Video Object Detection: What Average Precision Fails to Tell
Huizi Mao, Xiaodong Yang, William J. Dally
Average precision (AP) is a widely used metric to evaluate detection accuracy of image and video object detectors. In this paper, we analyze object detection from videos and point…
CaTDet: Cascaded Tracked Detector for Efficient Object Detection from Video
Huizi Mao, Taeyoung Kong, William J. Dally
Detecting objects in a video is a compute-intensive task. In this paper we propose CaTDet, a system to speedup object detection by leveraging the temporal correlation in video. CaT…
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Yujun Lin, Song Han, Huizi Mao +2
Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-…
Exploring the Regularity of Sparse Structure in Convolutional Neural Networks
Huizi Mao, Song Han, Jeff Pool +4
Sparsity helps reduce the computational complexity of deep neural networks by skipping zeros. Taking advantage of sparsity is listed as a high priority in next generation DNN accel…
Trained Ternary Quantization
Chenzhuo Zhu, Song Han, Huizi Mao +1
Deep neural networks are widely used in machine learning applications. However, the deployment of large neural networks models can be difficult to deploy on mobile devices with lim…
ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA
Song Han, Junlong Kang, Huizi Mao +9
Long Short-Term Memory (LSTM) is widely used in speech recognition. In order to achieve higher prediction accuracy, machine learning scientists have built larger and larger models.…
DSD: Dense-Sparse-Dense Training for Deep Neural Networks
Song Han, Jeff Pool, Sharan Narang +9
Modern deep neural networks have a large number of parameters, making them very hard to train. We propose DSD, a dense-sparse-dense training flow, for regularizing deep neural netw…
EIE: Efficient Inference Engine on Compressed Deep Neural Network
Song Han, Xingyu Liu, Huizi Mao +4
State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded…
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
Song Han, Huizi Mao, William J. Dally
Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limita…