Machine Learning Blog

In this blog post, read about advancements in ML with Arm NN 2021.

As first step to deploy your NN model on Ethos-U, Vela Compiler is open-source and easy-use. Try it out and embrace the improvement of ML ability brought by Ethos-U to embedded system.

On-device automatic speech recognition is now becoming feasible and is useful in scenarios without internet connection or when data privacy is a concern.

This year’s Arm DevSummit Developer Competition winner explains how his wildfire detection solution uses tech to tackle real-life sustainability issues.

This Blog mainly introduced how Paddle-Lite improves performance of mobile devices with Arm technology, especially for CPU and Mali GPU.

Learn more about how the future of smart camera technology will bring with it profound transformations in the way we interact with each other and the world around us.

Build and deploy embedded machine learning applications for Arm Cortex-M55 CPU and Arm Ethos-U55 NPU with Arm ML Embedded Evaluation Kit.

Learn more about how to use Arm Streamline to profile different machine learning workloads for your applications.

Step-by-step explainer about how to create intelligent NPCs in games through Unity's ML agents toolkit.

On-device face recognition with RGB cameras on digital televisions (DTVs) is set to become an increasingly important feature for users. Read more in this post.

In this blog we present a TinyML image recognition demo, on an Arm Cortex-M processor, using TensorFlow along with CMSIS-NN for optimized performance.

In this guest blog post, from Kentaro Niikura, read about how Renesas is implementing an advancement in AI.

Most conventional FPGA-based accelerators use off-chip memory for data transformation, then perform computation for a single-layer in a time-multiplexed manner. Throughput is often limited by the memory…

How to convert models to run Ethos-U55 and Ethos-U65 for models trained in PyTorch.

This blog discusses the growth of smartwatches and how Arm is delivering processor power and efficiency capabilities for these devices.

Demand for compute is increasing, yet carbon emissions must be reduced. Machine learning can make workflows more efficient, reducing their carbon footprint.

TinyML seeks to deploy ML algorithms on ultra low power systems, to enable us to intelligently select which data to transmit, improving energy efficiency.

IoT devices collect and transmit data to the cloud, where it is then analyzed and ML models trained. Privacy challenges arise when users are reluctant to share their personal data.