FPGA-based Trainable Autoencoder for Communication Systems
Abstract
Index Terms
- FPGA-based Trainable Autoencoder for Communication Systems
Recommendations
An FPGA-based Fine Tuning Accelerator for a Sparse CNN
FPGA '19: Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate ArraysFine-tuning learns abundant feature expression for a wide range of natural images by using a pre-trained CNN model. It can be applied to a wide range of the neural network (NN)based computer vision problems. This paper proposes an FPGA-based fine-tuning ...
Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks
FPGA '16: Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate ArraysConvolutional Neural Networks (CNNs) have gained popularity in many computer vision applications such as image classification, face detection, and video analysis, because of their ability to train and classify with high accuracy. Due to multiple ...
Real-time embedded systems powered by FPGA dynamic partial self-reconfiguration: a case study oriented to biometric recognition applications
This work aims to pave the way for an efficient open system architecture applied to embedded electronic applications to manage the processing of computationally complex algorithms at real-time and low-cost. The target is to define a standard ...
Comments
Information & Contributors
Information
Published In
![cover image ACM Conferences](/cms/asset/70aa7c9f-e696-405e-9f53-f643ba822b11/3490422.cover.jpg)
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Poster
Funding Sources
- Bundesministerium für Bildung und Forschung
Conference
Acceptance Rates
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in