Evaluating and implementing deep learning processor alternatives for vision

Convolutional neural networks (CNNs) and other deep learning techniques, as I’ve recently noted, are quickly becoming legitimate options for implementing object recognition and other computer vision capabilities. With the growing popularity of CNNs, there’s a growing range of processor options being used to deploy these algorithms. [Native Advertisement] Three talks presented at the recent Embedded Vision Summit showcase several of these processor options, and provide numerous ideas for creating efficient CNN implementations.  I’ll introduce the three presentation videos here – and for those who want a hands-on technical introduction to deep learning for computer vision, see the information about an upcoming live tutorial at the end of this column. In “Fast Deployment of Low-power Deep Learning on CEVA Vision Processors,” Yair Siegel of CEVA discusses how to implement deep learning…


Link to Full Article: Evaluating and implementing deep learning processor alternatives for vision