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Convolutional Neural Networks (CNNs) are compute-intensive, with increasingly complex architectures. Join us to discover how the new class of Winograd Algorithms can make CNNs faster than ever before, allowing workloads such as classification and recognition to be implemented on low-power, Arm-based platforms.
Machine learning (ML) processing requirements vary significantly according to workload; there is no one-size-fits-all solution. This webinar discusses how to choose the best ML software and hardware combination for your use case.
This webinar elaborates on real industry use-cases where the adoption of optimized low-level primitives for Arm processors has enabled improved performance and optimal use of heterogeneous system resources.
Learn how Arm NN and CMSIS-NN can help you develop efficient neural network applications for Cortex-M devices - and how the powerful i.MX RT processors can be used with CMSIS-NN to run applications like keyword spotting.