Summary:

This training topic covers essential information on Arm’s IP solutions for optimizing Machine Learning (ML) applications for Arm hardware. The topic introduces Arm’s solutions for implementing ML on Android and Linux platforms with our suite of ML software tools and how they relate to corresponding Arm hardware.

The topic provides an overview of Arm Compute Library, an open source toolkit for implementing ML applications on Arm’s Mali GPU’s.

Because Arm’s ML solution is optimized for Convolutional Neural Networks (CNNs), the course also provides an introduction to (CNNs) and how they operate. Finally, the course has some tutorials for you to follow that demonstrate how to run some ML example applications on Arm hardware, including Arm Cortex-A, Arm Cortex-M, and Arm Mali GPUs.

Prerequisites:

• A basic understanding of neural networks

Audience:

The course is relevant to anyone who wants to start targeting Machine Learning applications on Arm hardware IP.

Delivery Method:

• Online

Length:

• 1 hour

Modules:

Arm IP and Machine Learning

• Intro to Arm's machine learning IP
• Arm SW Library - Arm NN
• Arm SW Library - ACL and CMSIS-NN
• Arm ML Hardware

Introduction to Machine Learning

• Introducing Convolutional Neural Networks (CNNs)
• How does convolution work?
• Convolution - stride and padding
• Convolution - channels
• Convolutional Neural Networks (CNNS) in action
• Transfer learning

Introduction to Object Detection

• Introduction to Object Detection
• Bounding boxes
• Sliding windows
• Region proposals
• Landmark detection
• YOLO - You Only Look Once
• Anchor boxes

OpenCL Concepts

• Intro to OpenCL
• OpenCL Execution Model
• OpenCL example

How-to run ML tasks on Arm hardware

• Overview
• Image recognition on Arm Cortex-M with CMSIS-NN
• Running and profiling Arm NN on the HiKey 960 (Arm Cortex-A)
• Running and profiling Arm NN on the HiKey 960 (Arm Mali GPU)

Language: This course is presented in English.

Delivery Method: Bitesized video content

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You will have 3 months access to this community.