Recognition of convolutional neural network based on CUDA Technology
This work demonstrates GPU applicability for neural network tasks, offering significant speed gains for researchers and practitioners in high-performance computing.
This paper tackled the problem of applying GPU stream processors to neural networks by proposing a parallel recognition algorithm for Convolutional Neural Networks using CUDA technology, achieving a speed improvement of nearly 60 times compared to CPU-based serial algorithms.
For the problem whether Graphic Processing Unit(GPU),the stream processor with high performance of floating-point computing is applicable to neural networks, this paper proposes the parallel recognition algorithm of Convolutional Neural Networks(CNNs).It adopts Compute Unified Device Architecture(CUDA)technology, definite the parallel data structures, and describes the mapping mechanism for computing tasks on CUDA. It compares the parallel recognition algorithm achieved on GPU of GTX200 hardware architecture with the serial algorithm on CPU. It improves speed by nearly 60 times. Result shows that GPU based the stream processor architecture ate more applicable to some related applications about neural networks than CPU.