In this thesis I look into how one can train and run an artificial neural network using Compute Shader and what kind of performance can be expected. An artificial neural network is a computational model that is inspired by biological neural networks, e.g. a brain. Finding what kind of performance can be expected was done by creating an implementation that uses Compute Shader and then compare it to the FANN library, i.e. a fast artificial neural network library written in C. The conclusion is that you can improve performance by training an artificial neural network on the compute shader as long as you are using non-trivial datasets and neural network configurations
Abstract. This work presents the implementation of Feedforward Multi-Layer Perceptron (FFMLP) Neural...
Research areas: Approximate Computing, Computer Architecture, Neural Processing Unit, Accelerator De...
AbstractAlthough volunteer computing with a huge number of high-performance game consoles connected ...
The Graphics Processing Unit (GPU) parallel architecture is now being used not just for graphics but...
Neural networks (NNs) have been used in several areas, showing their potential but also their limita...
This thesis deals with the implementation of an application for artificial neural networks simulatio...
When asked to implement a neural network application, the decision concerning what hardware platform...
This paper makes two principal contributions. The first is that there appears to be no previous a de...
Automatic classification becomes more and more in- teresting as the amount of available data keeps g...
Simulating biological neural networks is an important task for computational neuroscientists attempt...
The article discusses possibilities of implementing a neural network in a parallel way. The issues o...
Modern graphics processing units (GPU) are used for much more than simply 3D graphics applications. ...
Graph Neural Networks (GNNs) are an important tool for extracting value from relational and unstruct...
Neural networks stand out from artificial intelligence because they can complete challenging tasks, ...
The ability to train large-scale neural networks has resulted in state-of-the-art per-formance in ma...
Abstract. This work presents the implementation of Feedforward Multi-Layer Perceptron (FFMLP) Neural...
Research areas: Approximate Computing, Computer Architecture, Neural Processing Unit, Accelerator De...
AbstractAlthough volunteer computing with a huge number of high-performance game consoles connected ...
The Graphics Processing Unit (GPU) parallel architecture is now being used not just for graphics but...
Neural networks (NNs) have been used in several areas, showing their potential but also their limita...
This thesis deals with the implementation of an application for artificial neural networks simulatio...
When asked to implement a neural network application, the decision concerning what hardware platform...
This paper makes two principal contributions. The first is that there appears to be no previous a de...
Automatic classification becomes more and more in- teresting as the amount of available data keeps g...
Simulating biological neural networks is an important task for computational neuroscientists attempt...
The article discusses possibilities of implementing a neural network in a parallel way. The issues o...
Modern graphics processing units (GPU) are used for much more than simply 3D graphics applications. ...
Graph Neural Networks (GNNs) are an important tool for extracting value from relational and unstruct...
Neural networks stand out from artificial intelligence because they can complete challenging tasks, ...
The ability to train large-scale neural networks has resulted in state-of-the-art per-formance in ma...
Abstract. This work presents the implementation of Feedforward Multi-Layer Perceptron (FFMLP) Neural...
Research areas: Approximate Computing, Computer Architecture, Neural Processing Unit, Accelerator De...
AbstractAlthough volunteer computing with a huge number of high-performance game consoles connected ...