AI summary

This paper introduces a GPU-based implementation of neural networks for enhanced performance in text detection, achieving a 20-fold speedup on an ATI RADEON 9700 PRO by converting inner-product operations into matrix operations, which efficiently utilizes the parallelism of GPUs.

Abstract

Graphics processing unit (GPU) is used for a faster artificial neural network. It is used to implement the matrix multiplication of a neural network to enhance the time performance of a text detection system. Preliminary results produced a 20-fold performance enhancement using an ATI RADEON 9700 PRO board. The parallelism of a GPU is fully utilized by accumulating a lot of input feature vectors and weight vectors, then converting the many inner-product operations into one matrix operation. Further research areas include benchmarking the performance with various hardware and GPU-aware learning algorithms.