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Found 10 papers, 4 papers with code
Gegelati: Lightweight Artificial Intelligence through Generic and Evolvable Tangled Program Graphs
The impact of customizable instructions on the outcome of a training process is demonstrated on a state-of-the-art reinforcement learning environment.
NTIRE 2020 Challenge on Image Demoireing: Methods and Results
Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image.
NoiseBreaker: Gradual Image Denoising Guided by Noise Analysis
When the target noise is complex, e. g. composed of an unknown mixture of primary noises with unknown intensity, fully supervised solutions are limited by the difficulty to build a suited training set for the problem.
OpenDenoising: an Extensible Benchmark for Building Comparative Studies of Image Denoisers
Image denoising has recently taken a leap forward due to machine learning.
Electro-Magnetic Side-Channel Attack Through Learned Denoising and Classification
This paper proposes an upgraded electro-magnetic side-channel attack that automatically reconstructs the intercepted data.
The Challenge of Multi-Operand Adders in CNNs on FPGAs: How not to solve it!
Convolutional Neural Networks (CNNs) are computationally intensive algorithms that currently require dedicated hardware to be executed.
Distributed, Parallel, and Cluster Computing Hardware Architecture
Accelerating CNN inference on FPGAs: A Survey
This paper presents a state-of-the-art of CNN inference accelerators over FPGAs.
Tactics to Directly Map CNN graphs on Embedded FPGAs
Deep Convolutional Neural Networks (CNNs) are the state-of-the-art in image classification.
Hardware Automated Dataflow Deployment of CNNs
Deep Convolutional Neural Networks (CNNs) are the state of the art systems for image classification and scene understating.
Other Computer Science
A Holistic Approach for Optimizing DSP Block Utilization of a CNN implementation on FPGA
Deep Neural Networks are becoming the de-facto standard models for image understanding, and more generally for computer vision tasks.
