130 citations · 222 across the 7 of their papers we have counts for
9 papers
TSM: Temporal Shift Module for Efficient and Scalable Video Understanding on Edge Device
Ji Lin, Chuang Gan, Kuan Wang +1
The explosive growth in video streaming requires video understanding at high accuracy and low computation cost. Conventional 2D CNNs are computationally cheap but cannot capture te…
Anycost GANs for Interactive Image Synthesis and Editing
Ji Lin, Richard Zhang, Frieder Ganz +2
Generative adversarial networks (GANs) have enabled photorealistic image synthesis and editing. However, due to the high computational cost of large-scale generators (e.g., StyleGA…
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…
MCUNet: Tiny Deep Learning on IoT Devices
Ji Lin, Wei-Ming Chen, Yujun Lin +3
Machine learning on tiny IoT devices based on microcontroller units (MCU) is appealing but challenging: the memory of microcontrollers is 2-3 orders of magnitude smaller even than…
APQ: Joint Search for Network Architecture, Pruning and Quantization Policy
Tianzhe Wang, Kuan Wang, Han Cai +3
We present APQ for efficient deep learning inference on resource-constrained hardware. Unlike previous methods that separately search the neural architecture, pruning policy, and q…