Discover tips and techniques for your Arm-based machine learning projects with our library of webinars

Even faster CNNs: exploring the new class of winograd algorithms

Convolutional Neural Networks (CNNs) are compute-intensive, with increasingly complex architectures. Learn how the new class of Winograd Algorithms make CNNs faster than before, allowing implementation of workloads like classification and recognition on low-power, Arm-based platforms.

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Compute Library: optimizing computer vision and machine learning on Arm

Explore industry use cases in which the adoption of optimized low-level primitives for Arm processors enabled improved performance and optimal use of heterogeneous system resources.

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Jump-start machine learning projects with CMSIS-NN on NXP i.MX RT

Learn how to use Arm NN and CMSIS-NN to develop efficient neural network applications for Cortex-M devices. Explore how to use i.MX RT processors with CMSIS-NN to run applications like keyword spotting.

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Accelerate machine learning using Compute Library and HiKey 960

Learn how to run AI and ML applications on a mobile development platform.




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Machine learning on deeply embedded and resource-constrained end nodes for the IoT

Learn how hardware technology and software libraries help developers implement tasks like voice recognition without connecting to the cloud.

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How to use the MATRIX Creator to unleash the power of voice recognition

Learn how to use the MATRIX Creator to unleash the power of voice recognition. Deploy the Snips voice assistant on the MATRIX Creator using the MATRIX Core programming layer in JavaScript.

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How to do edge computing everywhere with a mobile developer workstation

Learn how to do edge computing with a solar-powered mobile developer workstation and extreme edge computing box with 96Boards and miniNodes.

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Enabling industry 4.0 with NXP's MCU-based machine learning solution for power-conscious end nodes

Learn how to make the most of NXP’s Arm Cortex-M33-based MCU for your power-conscious machine learning application.

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Running and profiling Arm NN on the HiKey 960

Learn how to create Linux applications that load TensorFlow trained Neural Network models, run them on Arm Cortex-A CPUs and Mali GPUs, and profile application performance with Arm Streamline.

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Image recognition on Arm Cortex-M with CMSIS-NN

Learn how to perform real-time image recognition on a low-power Arm Cortex-M7 processor using the Arm CMSIS-NN library.

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Scalable ML acceleration with ONNX Runtime

Technical session delivered by Manash Goswami from Microsoft demonstrates how the ONNX Runtime Execution Providers can run on multiple hardware configurations using a single API.

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Running AI and Neural networks on microcontrollers made simple with the STM32Cube.AI

Markus Mayr from STMicroelectronics discusses the STM32Cube.AI toolbox which generates optimized code to run neural networks on the STM32 Arm Cortex-M based microcontrollers

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Maximize the performance of your ML platform with Arm 

Explore how to use Arm’s ML software libraries and tools optimized for Arm endpoint devices.

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Building noise-immune speech interfaces for IoT

Chris Rowen, CEO at BabbleLabs, covers the Clear Command software subsystems that run on Arm Cortex-M processors.

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Extending machine learning to industrial IoT applications at the edge with AWS

Ian Perez Ponce from AWS discusses real-world use case trends for Industrial IoT (IIoT) applications.

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Swim builds AI models and predicts in real-time

Dr. Simon Crosby, CTO at Swim.ai, shows how Swim automatically builds, runs and manages scalable, distributed, intelligent dataflow pipelines

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Design and optimize computer vision applications on Arm

Dr. Nitin Gupta from Dori AI covers what to consider when trying to annotate, train, optimize, benchmark, deploy, and monitor a computer vision ML application.

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Next generation machine learning for mobile and embedded platforms

Dr. Rajen Bhatt from Qeexo shows the commercial uses of Qeexo’s engine and suite of tools for optimizing models.

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AI workflow for large scale deployment of far-edge ML devices

Kabir Manghnani from Shoreline IoT discusses simultaneous training and deployment of ML models trained specifically for the sensors they operate on.

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Accelerating ML inference on Raspberry Pi With PyArmNN

Create and optimize on-target run-time performance for advanced ML solutions using the Au-Zone Technologies DeepView ML Toolkit, across a broad spectrum of Arm IP.

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Community Forums

Suggested answer Pytorch framework for Arm NN (CMSIS)
  • Arm NN
  • CMSIS-NN
0 votes 2528 views 3 replies Latest 17 days ago by Karl Fezer Answer this
Suggested answer A good ML Conference 0 votes 2492 views 4 replies Latest 18 days ago by asksolutions01 Answer this
Not answered Example cpp running onnx on arm NN 0 votes 957 views 0 replies Started 1 months ago by Gushgush Answer this
Not answered Which CMSIS-DSP parts are used by CMSIS-NN
  • CMSIS
  • CMSIS-NN
0 votes 530 views 0 replies Started 2 months ago by trembel Answer this
Suggested answer Pytorch framework for Arm NN (CMSIS) Latest 17 days ago by Karl Fezer 3 replies 2528 views
Suggested answer A good ML Conference Latest 18 days ago by asksolutions01 4 replies 2492 views
Not answered Example cpp running onnx on arm NN Started 1 months ago by Gushgush 0 replies 957 views
Not answered Which CMSIS-DSP parts are used by CMSIS-NN Started 2 months ago by trembel 0 replies 530 views