Gian Marco Iodice, Senior Software Engineer in the Machine Learning Group at Arm, presents the “Even Faster CNNs: Exploring the New Class of Winograd Algorithms” tutorial at the May 2018 Embedded Vision Summit.
Over the past decade, deep learning networks have revolutionized the task of classification and recognition in a broad area of applications. Deeper and more accurate networks have been proposed every year and more recent developments have shown how these workloads can be implemented on modern low-power embedded platforms. This presentation discusses a recently introduced class of algorithms to reduce the arithmetic complexity of convolution layers with small filter sizes. After an introduction to the latest optimizations techniques for the most common solutions such as GEMM, the talk dives deeply into the design of Winograd algorithms, analyzing the complexity and the performance achieved for convolutional neural networks.
