6.5k citations · 10.4k across the 118 of their papers we have counts for
8 papers · 2 filters
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
Bichen Wu, Xiaoliang Dai, Peizhao Zhang +7
Designing accurate and efficient ConvNets for mobile devices is challenging because the design space is combinatorially large. Due to this, previous neural architecture search (NAS…
Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search
Bichen Wu, Yanghan Wang, Peizhao Zhang +3
Recent work in network quantization has substantially reduced the time and space complexity of neural network inference, enabling their deployment on embedded and mobile devices wi…
Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs
Yifan Yang, Qijing Huang, Bichen Wu +8
Using FPGAs to accelerate ConvNets has attracted significant attention in recent years. However, FPGA accelerator design has not leveraged the latest progress of ConvNets. As a res…
A Novel Domain Adaptation Framework for Medical Image Segmentation
Amir Gholami, Shashank Subramanian, Varun Shenoy +6
We propose a segmentation framework that uses deep neural networks and introduce two innovations. First, we describe a biophysics-based domain adaptation method. Second, we propose…
SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud
Bichen Wu, Xuanyu Zhou, Sicheng Zhao +2
Earlier work demonstrates the promise of deep-learning-based approaches for point cloud segmentation; however, these approaches need to be improved to be practically useful. To thi…
A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving
Xiangyu Yue, Bichen Wu, Sanjit A. Seshia +2
3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point…