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About Restricted Boltzmann Machines On Multi-Core Processors: by Sai Prasad Nooka Stavan Karia
The document discusses implementing Restricted Boltzmann Machines (RBMs) on multi-core processors and GPUs. RBMs are a type of neural network that can be used for tasks like classification and feature learning. The implementation involves developing CUDA kernels for computing the hidden and visible unit activations and updating the weights. Testing on an NVIDIA GPU showed speedups of 22-46x compared to a CPU implementation for training RBMs on MNIST data with up to 60,000 samples and 800 hidden units per epoch. Using a GPU allows training larger models more efficiently by taking advantage of parallel processing.
Detailed Information
| Author: | ['npnbkck'] |
|---|---|
| Publication Year: | 2016 |
| Pages: | 31 |
| Language: | English |
| Format: | |
| Price: | FREE |
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