355 citations · 980 across the 44 of their papers we have counts for
5 papers · 1 filter
CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression
Lukas Cavigelli, Pascal Hager, Luca Benini
Lossy image compression algorithms are pervasively used to reduce the size of images transmitted over the web and recorded on data storage media. However, we pay for their high com…
Computationally Efficient Target Classification in Multispectral Image Data with Deep Neural Networks
Lukas Cavigelli, Dominic Bernath, Michele Magno +1
Detecting and classifying targets in video streams from surveillance cameras is a cumbersome, error-prone and expensive task. Often, the incurred costs are prohibitive for real-tim…
Deep Structured Features for Semantic Segmentation
Michael Tschannen, Lukas Cavigelli, Fabian Mentzer +2
We propose a highly structured neural network architecture for semantic segmentation with an extremely small model size, suitable for low-power embedded and mobile platforms. Speci…
YodaNN: An Architecture for Ultra-Low Power Binary-Weight CNN Acceleration
Renzo Andri, Lukas Cavigelli, Davide Rossi +1
Convolutional neural networks (CNNs) have revolutionized the world of computer vision over the last few years, pushing image classification beyond human accuracy. The computational…
Origami: A 803 GOp/s/W Convolutional Network Accelerator
Lukas Cavigelli, Luca Benini
An ever increasing number of computer vision and image/video processing challenges are being approached using deep convolutional neural networks, obtaining state-of-the-art results…