| Publisher | Microsoft | ||
|---|---|---|---|
| Format | 133.6KB PDF | Date added | 28 Feb 2006 |
| Topics | Processors, Network Administration | ||
| Downloads | 43 | ||
Convolutional Neural Networks (CNNs) are well known for producing state-of-the-art recognizers for document processing. However, they can be difficult to implement and are usually slower than traditional Multi-Layer Perceptrons (MLPs). This paper presents three novel approaches to speeding up CNNs: unrolling convolution, using BLAS (basic linear algebra subroutines), and using GPUs (Graphic Processing Units). Unrolled convolution converts the processing in each convolutional layer (both forward-propagation and back-propagation) into a matrix-matrix product. The matrix-matrix product representation of CNNs makes their implementation as easy as MLPs. BLAS is used to efficiently compute matrix products on the CPU.
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