163 citations · 173 across the 5 of their papers we have counts for
8 papers
Freely scalable and reconfigurable optical hardware for deep learning
Liane Bernstein, Alexander Sludds, Ryan Hamerly +3
As deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available comp…
Depth Map Estimation of Dynamic Scenes Using Prior Depth Information
James Noraky, Vivienne Sze
Depth information is useful for many applications. Active depth sensors are appealing because they obtain dense and accurate depth maps. However, due to issues that range from powe…
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…
FSMI: Fast computation of Shannon Mutual Information for information-theoretic mapping
Zhengdong Zhang, Trevor Henderson, Sertac Karaman +1
Exploration tasks are embedded in many robotics applications, such as search and rescue and space exploration. Information-based exploration algorithms aim to find the most informa…
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso +66
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains…
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 '…