163 citations · 354 across the 12 of their papers we have counts for
8 papers · 1 filter
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…
SegSort: Segmentation by Discriminative Sorting of Segments
Jyh-Jing Hwang, Stella X. Yu, Jianbo Shi +4
Almost all existing deep learning approaches for semantic segmentation tackle this task as a pixel-wise classification problem. Yet humans understand a scene not in terms of pixels…
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…
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang +1
Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs del…
Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning
Tien-Ju Yang, Yu-Hsin Chen, Vivienne Sze
Deep convolutional neural networks (CNNs) are indispensable to state-of-the-art computer vision algorithms. However, they are still rarely deployed on battery-powered mobile device…