Mohammad Rastegari, Chief Technology Officer at Xnor.ai, presents the "Methods for Creating Efficient Convolutional Neural Networks" tutorial at the May 2019 Embedded Vision Summit.
In the past few years, convolutional neural networks (CNNs) have revolutionized several application domains in AI and computer vision. The biggest challenge with state-of-the-art CNNs is the massive compute demands that prevent these models from being used in many embedded systems and other resource-constrained environments.
In this talk, Rastegari explains and contrasts several recent techniques that enable CNN models with high accuracy to consume very little memory and processor resources. These methods include a variety of algorithmic and optimization approaches to deep learning models. Quantization, sparsification and compact model design are three of the major techniques for efficient CNNs, which are discussed in the context of computer vision applications including detection, recognition and segmentation.
