- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Dimensions
-
31 cm
- Extent
-
114 S.
- Language
-
Englisch
- Notes
-
Ill., graph. Darst.
Dresden, Techn. Univ., Diss., 2012
- Creator
- Table of contents
- Rights
-
Bei diesem Objekt liegt nur das Inhaltsverzeichnis digital vor. Der Zugriff darauf ist unbeschränkt möglich.
- Last update
-
11.06.2025, 1:59 PM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Object type
- Hochschulschrift
Associated
Other Objects (12)
Trace-based Performance Analysis for Hardware Accelerators
High-Performance Accurate and Approximate Multipliers for FPGA-Based Hardware Accelerators
Deployment of Deep Neural Networks on Dedicated Hardware Accelerators
Corporate accelerators: design and startup performance
Rapid Prototyping for Hardware Accelerators in the Medical Imaging Domain
Toolflow for the algorithm-hardware co-design of memristive ANN accelerators
PIConGPU: predictive simulations of laser-particle accelerators with manycore hardware
Efficiency Versus Accuracy: A Review of Design Techniques for DNN Hardware Accelerators
Comparing and modelling the performance of different ML frameworks and hardware accelerators in a coupled OpenFoam+ML application
Database accelerators
Induction accelerators
Machine Learning Based Optimization of Laser-Plasma Accelerators
Trace-based Performance Analysis for Hardware Accelerators
High-Performance Accurate and Approximate Multipliers for FPGA-Based Hardware Accelerators
Deployment of Deep Neural Networks on Dedicated Hardware Accelerators
Corporate accelerators: design and startup performance
Rapid Prototyping for Hardware Accelerators in the Medical Imaging Domain
Toolflow for the algorithm-hardware co-design of memristive ANN accelerators
PIConGPU: predictive simulations of laser-particle accelerators with manycore hardware
Efficiency Versus Accuracy: A Review of Design Techniques for DNN Hardware Accelerators
Comparing and modelling the performance of different ML frameworks and hardware accelerators in a coupled OpenFoam+ML application
Database accelerators
Induction accelerators
Machine Learning Based Optimization of Laser-Plasma Accelerators
Trace-based Performance Analysis for Hardware Accelerators
High-Performance Accurate and Approximate Multipliers for FPGA-Based Hardware Accelerators
Deployment of Deep Neural Networks on Dedicated Hardware Accelerators
Corporate accelerators: design and startup performance
Rapid Prototyping for Hardware Accelerators in the Medical Imaging Domain
Toolflow for the algorithm-hardware co-design of memristive ANN accelerators
PIConGPU: predictive simulations of laser-particle accelerators with manycore hardware
Efficiency Versus Accuracy: A Review of Design Techniques for DNN Hardware Accelerators
Comparing and modelling the performance of different ML frameworks and hardware accelerators in a coupled OpenFoam+ML application
Database accelerators
Induction accelerators