Optimizing Convolution Neural Network on the TI C6678 multicore DSP

摘要：

　　Convolutional Neural Networks(CNNs)have become the most advanced algorithms for deep learning.They are widely used in image processing,object detection and automatic translation.As the demand for CNNs continues to increase,the platforms on which they are deployed continue to expand.As an excellent low-power,high-performance,embedded solution,Digital Signal Processor(DSP)is used frequently in many key areas.This paper attempts to deploy the CNN to Texas Instruments(TI)s TMS320C6678 multi-core DSP and optimize the main operations(convolution)to accommodate the DSP structure.The efficiency of the improved convolution operation has increased by tens of times.

作者: Guozhao Zeng Xiao Hu Yueyue Chen

作者单位: College of Computer,National University of Defence Technology,Changsha,Hunan Province,China

会议类型: 国际会议

会议名称: 2018 International Symposium on Water System Operations(ISWRSO 2018)(2018年水资源系统及调度国际研讨会)

会议地点: 北京

会议语种:英文

页码: 1-5

在线出版日期: 2018-10-12（万方平台首次上网日期，不代表论文的发表时间）