"Tailoring Convolutional Neural Networks for Low-Cost, Low-Power Implementation," a Presentation From Synopsys
Bruno Lavigueur, Project Leader for Embedded Vision at Synopsys, presents the "Tailoring Convolutional Neural Networks for Low-Cost, Low-Power Implementation" tutorial at the May 2015 Embedded Vision Summit.
Deep learning-based object detection using convolutional neural networks (CNN) has recently emerged as one of the leading approaches for achieving state-of-the-art detection accuracy for a wide range of object classes. Most of the current CNN-based detection algorithm implementations run on high-performance computing platforms that include high-end general-purpose processors and GP-GPUs. These CNN implementations have significant computing power and memory requirements.
Bruno presents Synopsys' experience in reducing the complexity of the CNN graph to make the resulting algorithm amenable to low-cost and low-power computing platforms. This involves reducing the compute requirements, memory size for storing convolution coefficients, and moving from floating point to 8 and 16 bit fixed point data widths. Bruno demonstrates results for a face detection application running on a dedicated low-cost and low-power multi-core platform optimized for CNN-based applications.