AI summary

This paper introduces three novel approaches to speeding up CNNs: unrolling convolution, using BLAS, and using GPUs. The results on character recognition problems indicate that unrolled convolution with BLAS produces a dramatic 2.4X-3.0X speedup and the GPU implementation produces a 3.1X–4.1X speedup.

Abstract

Convolutional neural networks (CNNs) are well known for producing state-of-the-art recognizers for document processing [1]. However, they can be difficult to implement and are usually slower than traditional multi-layer perceptrons (MLPs). We present three novel approaches to speeding up CNNs: a) unrolling convolution, b) using BLAS (basic linear algebra subroutines), and c) 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. We also present a pixel shader based GPU implementation of CNNs. Results on character recognition problems indicate that unrolled convolution with BLAS produces a dramatic 2.4X-3.0X speedup. The GPU implementation is even faster and produces a 3.1X–4.1X speedup.