163 citations · 192 across the 10 of their papers we have counts for
7 papers · 1 filter
Searching for Efficient Multi-Stage Vision Transformers
Yi-Lun Liao, Sertac Karaman, Vivienne Sze
Vision Transformer (ViT) demonstrates that Transformer for natural language processing can be applied to computer vision tasks and result in comparable performance to convolutional…
NetAdaptV2: Efficient Neural Architecture Search with Fast Super-Network Training and Architecture Optimization
Tien-Ju Yang, Yi-Lun Liao, Vivienne Sze
Neural architecture search (NAS) typically consists of three main steps: training a super-network, training and evaluating sampled deep neural networks (DNNs), and training the dis…
Design Considerations for Efficient Deep Neural Networks on Processing-in-Memory Accelerators
Tien-Ju Yang, Vivienne Sze
This paper describes various design considerations for deep neural networks that enable them to operate efficiently and accurately on processing-in-memory accelerators. We highligh…
DeeperLab: Single-Shot Image Parser
Tien-Ju Yang, Maxwell D. Collins, Yukun Zhu +6
We present a single-shot, bottom-up approach for whole image parsing. Whole image parsing, also known as Panoptic Segmentation, generalizes the tasks of semantic segmentation for '…
FastDepth: Fast Monocular Depth Estimation on Embedded Systems
Diana Wofk, Fangchang Ma, Tien-Ju Yang +2
Depth sensing is a critical function for robotic tasks such as localization, mapping and obstacle detection. There has been a significant and growing interest in depth estimation f…
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications
Tien-Ju Yang, Andrew Howard, Bo Chen +5
This work proposes an algorithm, called NetAdapt, that automatically adapts a pre-trained deep neural network to a mobile platform given a resource budget. While many existing algo…