192 citations · 638 across the 26 of their papers we have counts for
12 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…
Towards Closing the Energy Gap Between HOG and CNN Features for Embedded Vision
Amr Suleiman, Yu-Hsin Chen, Joel Emer +1
Computer vision enables a wide range of applications in robotics/drones, self-driving cars, smart Internet of Things, and portable/wearable electronics. For many of these applicati…
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…
Hardware for Machine Learning: Challenges and Opportunities
Vivienne Sze, Yu-Hsin Chen, Joel Emer +2
Machine learning plays a critical role in extracting meaningful information out of the zetabytes of sensor data collected every day. For some applications, the goal is to analyze a…
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…
A 58.6mW Real-Time Programmable Object Detector with Multi-Scale Multi-Object Support Using Deformable Parts Model on 1920x1080 Video at 30fps
Amr Suleiman, Zhengdong Zhang, Vivienne Sze
This paper presents a programmable, energy-efficient and real-time object detection accelerator using deformable parts models (DPM), with 2x higher accuracy than traditional rigid…
FAST: A Framework to Accelerate Super-Resolution Processing on Compressed Videos
Zhengdong Zhang, Vivienne Sze
State-of-the-art super-resolution (SR) algorithms require significant computational resources to achieve real-time throughput (e.g., 60Mpixels/s for HD video). This paper introduce…