Add to ORKGAutomatic classification becomes more and more in- teresting as the amount of available data keeps grow- ing. Also, modern computers are equipped with pow- erful hardware specifically designed for processing fast amounts of data, namely the GPU (graphical process- ing unit). We use OpenCL to implement a multilayer perceptron that runs on the GPU. Our implementation scales better than an implementation for the CPU, but this proves to be only an improvement for larger net- works due to the overhead of calculating on a different device.
Research areas: Approximate Computing, Computer Architecture, Neural Processing Unit, Accelerator De...
BackgroundModern neuroscience research demands computing power. Neural circuit mapping studies such ...
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...
Recently, General Purpose Graphical Processing Units (GP-GPUs) have been identified as an intriguing...
The Graphics Processing Unit (GPU) parallel architecture is now being used not just for graphics but...
Today's computer systems often contains several different processing units aside from the CPU. Among...
The decline of Moore’s law has led to a fundamental shift in the design of micro-processor architect...
During recent years General-Purpose Graphical Processing Units (GP-GPUs) have entered the field of H...
Graph Neural Networks (GNNs) are an important tool for extracting value from relational and unstruct...
When asked to implement a neural network application, the decision concerning what hardware platform...
Parallel programming is about performance, for otherwise we’d write a sequential program. A problem ...
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...
Using modern Graphic Processing Units (GPUs) becomes very useful for computing complex and time cons...
Research areas: Approximate Computing, Computer Architecture, Neural Processing Unit, Accelerator De...
BackgroundModern neuroscience research demands computing power. Neural circuit mapping studies such ...
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...
Recently, General Purpose Graphical Processing Units (GP-GPUs) have been identified as an intriguing...
The Graphics Processing Unit (GPU) parallel architecture is now being used not just for graphics but...
Today's computer systems often contains several different processing units aside from the CPU. Among...
The decline of Moore’s law has led to a fundamental shift in the design of micro-processor architect...
During recent years General-Purpose Graphical Processing Units (GP-GPUs) have entered the field of H...
Graph Neural Networks (GNNs) are an important tool for extracting value from relational and unstruct...
When asked to implement a neural network application, the decision concerning what hardware platform...
Parallel programming is about performance, for otherwise we’d write a sequential program. A problem ...
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...
Using modern Graphic Processing Units (GPUs) becomes very useful for computing complex and time cons...
Research areas: Approximate Computing, Computer Architecture, Neural Processing Unit, Accelerator De...
BackgroundModern neuroscience research demands computing power. Neural circuit mapping studies such ...
Simulating biological neural networks is an important task for computational neuroscientists attempt...