activity
20102019
most citedHardware for Machine Learning: Challenges and Opportunities

192 citations · 219 across the 6 of their papers we have counts for

collaborators

6 papers

cs.RO2019★ 4 cited

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…

cs.RO2018

Navion: A 2mW Fully Integrated Real-Time Visual-Inertial Odometry Accelerator for Autonomous Navigation of Nano Drones

Amr Suleiman, Zhengdong Zhang, Luca Carlone +2

This paper presents Navion, an energy-efficient accelerator for visual-inertial odometry (VIO) that enables autonomous navigation of miniaturized robots (e.g., nano drones), and vi…

cs.CV2016★ 192 cited

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…

cs.CV2016

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…

cs.CV2016★ 1 cited

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…

cs.CV2010★ 22 cited

TILT: Transform Invariant Low-rank Textures

Zhengdong Zhang, Arvind Ganesh, Xiao Liang +1

In this paper, we show how to efficiently and effectively extract a class of "low-rank textures" in a 3D scene from 2D images despite significant corruptions and warping. The low-r…