Discover tips and techniques for your Arm-based machine learning projects with our growing bank of webinars.
Running and profiling Arm NN on the HiKey 960
Learn how to create Linux applications which load TensorFlow trained Neural Network models, run them on Arm Cortex-A CPUs and Mali GPUs, and profile application performance with Arm Streamline.View
- Image recognition on Arm Cortex-M with CMSIS-NN
Even Faster CNNs: Exploring the New Class of Winograd Algorithms
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.
Project Trillium - Optimizing ML Performance for any Application
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.
Compute Library: Optimizing Computer Vision and Machine Learning on Arm
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.View on YouTube
Jump-Start Machine Learning Projects with CMSIS-NN on NXP i.MX RT
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.
Accelerate Machine Learning using Compute Library and HiKey 960
Join our experts and learn how fast and easy it is to run AI/ML applications on a mobile development platform.
Machine Learning on Deeply Embedded and Resource Constrained End Nodes for the IoT
This webinar discusses how today's hardware technology and software libraries help developers efficiently implement tasks like voice recognition, without connecting to the cloud.