114 citations · 323 across the 13 of their papers we have counts for
6 papers · 1 filter
Restructurable Activation Networks
Kartikeya Bhardwaj, James Ward, Caleb Tung +6
Is it possible to restructure the non-linear activation functions in a deep network to create hardware-efficient models? To address this question, we propose a new paradigm called…
Fast and Accurate: Video Enhancement using Sparse Depth
Yu Feng, Patrick Hansen, Paul N. Whatmough +2
This paper presents a general framework to build fast and accurate algorithms for video enhancement tasks such as super-resolution, deblurring, and denoising. Essential to our fram…
Mesorasi: Architecture Support for Point Cloud Analytics via Delayed-Aggregation
Yu Feng, Boyuan Tian, Tiancheng Xu +2
Point cloud analytics is poised to become a key workload on battery-powered embedded and mobile platforms in a wide range of emerging application domains, such as autonomous drivin…
ASV: Accelerated Stereo Vision System
Yu Feng, Paul Whatmough, Yuhao Zhu
Estimating depth from stereo vision cameras, i.e., "depth from stereo", is critical to emerging intelligent applications deployed in energy- and performance-constrained devices, su…
FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning
Paul N. Whatmough, Chuteng Zhou, Patrick Hansen +3
The computational demands of computer vision tasks based on state-of-the-art Convolutional Neural Network (CNN) image classification far exceed the energy budgets of mobile devices…
Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision
Yuhao Zhu, Anand Samajdar, Matthew Mattina +1
Continuous computer vision (CV) tasks increasingly rely on convolutional neural networks (CNN). However, CNNs have massive compute demands that far exceed the performance and energ…