130 citations · 199 across the 4 of their papers we have counts for
5 papers
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
Haotian Tang, Zhijian Liu, Shengyu Zhao +4
Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive safely. Given the limited hardware resources, existing 3D perception models are not able…
Hardware-Centric AutoML for Mixed-Precision Quantization
Kuan Wang, Zhijian Liu, Yujun Lin +2
Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference. Emergent DNN hardware accelerators begin to support mixed precision (1…
Lite Transformer with Long-Short Range Attention
Zhanghao Wu, Zhijian Liu, Ji Lin +2
Transformer has become ubiquitous in natural language processing (e.g., machine translation, question answering); however, it requires enormous amount of computations to achieve hi…
Point-Voxel CNN for Efficient 3D Deep Learning
Zhijian Liu, Haotian Tang, Yujun Lin +1
We present Point-Voxel CNN (PVCNN) for efficient, fast 3D deep learning. Previous work processes 3D data using either voxel-based or point-based NN models. However, both approaches…
Design Automation for Efficient Deep Learning Computing
Song Han, Han Cai, Ligeng Zhu +4
Efficient deep learning computing requires algorithm and hardware co-design to enable specialization: we usually need to change the algorithm to reduce memory footprint and improve…