192 citations · 730 across the 29 of their papers we have counts for
5 papers · 1 filter
SMAUG: End-to-End Full-Stack Simulation Infrastructure for Deep Learning Workloads
Sam Likun Xi, Yuan Yao, Kshitij Bhardwaj +3
In recent years, there has been tremendous advances in hardware acceleration of deep neural networks. However, most of the research has focused on optimizing accelerator microarchi…
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…
ISP4ML: Understanding the Role of Image Signal Processing in Efficient Deep Learning Vision Systems
Patrick Hansen, Alexey Vilkin, Yury Khrustalev +4
Convolutional neural networks (CNNs) are now predominant components in a variety of computer vision (CV) systems. These systems typically include an image signal processor (ISP), e…
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers
Igor Fedorov, Ryan P. Adams, Matthew Mattina +1
The vast majority of processors in the world are actually microcontroller units (MCUs), which find widespread use performing simple control tasks in applications ranging from autom…
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…