Hardware Pioneers 2024 - Realising AI at the Edge - Arrow

Dive into the 5 key steps to achieving AI at the Edge in this comprehensive video by a Technology Leader at Arrow, covering insights into Data Engineering, Model Training, Optimization, Deployment, and Monitoring.

How are AI vision systems transforming industries?

Discover the transformative power of AI vision systems in various industries. Explore real-life applications and learn why connectivity is crucial for their success.

Watch our Tech Snack to delve deeper into how AI is reshaping industries with its machine learning and machine vision capabilities, and how Molex solutions are facilitating these advancements.


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Develop for all six NVIDIA Jetson Orin modules with the power of one developer kit

In this article, learn how the new Jetson AGX Orin Developer Kit makes it possible to spread development effort across multiple modules to streamline processes and maximize efficiency.

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NXP eIQ® Neutron Neural Processing Unit

NXP eIQ for next generation of edge applications: Highly scalable, area and power efficient machine learning accelerator core architecture. NXP offers a very wide portfolio of devices from traditional MCUs in the Kinetis, LPC families and more recently the MCX portfolio of devices, to our i.MX RT crossover MCUs and our i.MX applications processors, and in each of the market areas we serve, we see an increased demand for efficient machine learning compute capabilities

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Visualizing the AI-driven future of automation

This article explores the recent developments and capabilities of AI, specifically focusing on machine vision. It highlights how AI is transforming industries such as healthcare, agriculture, manufacturing, and automotive.

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Accelerate Edge AI with Lattice sensAI

The Lattice sensAI solution stack is a comprehensive suite of tools and resources designed to accelerate the integration of flexible, low-power inferencing at the edge.

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Enhancing Machine Vision with 3M Expertise

3M offers connectors, cables, and assemblies for high-speed, high-density input/output applications in three major standards that cross the spectrum of machine vision.

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Silicon Labs adds AI acceleration
to wireless IoT chips

Explore Silicon Labs' latest wireless IoT chips featuring dedicated AI acceleration embedded on-chip through an in-house matrix vector processor design. Explore the practical applications driving the need for this advanced capability and learn how the accelerator seamlessly integrates with the device's deep sleep modes for optimal efficiency.

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Machine Learning Solutions from
Microchip Technology

Explore the cutting-edge world of Microchip Technology Machine Learning, where you are empowered to create and implement advanced models effortlessly. Whether you're venturing into the realm of Microcontroller Units (MCUs) and Microprocessor Units (MPUs) or seeking specialized tools for image classification and video applications, this comprehensive suite of solutions has you covered.

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Machine vision and interconnects

Amphenol enables Machine Vision applications by employing AI connectivity solutions that combine engineering expertise, high performance, and compact design.

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Advancing Industry with AI Machine
Visioning with TE Connectivity

Your applications connected with AI are made possible with TE Connectivity (TE). Miniaturization, increasing power requirements, the impact of artificial intelligence (AI), sustainability, and energy efficiency are evolving trends in the industry.

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EdgeAI computing inside MEMS sensors enhancing breakthrough innovations

Date: Monday, September 9, 2024
1:00 PM - 2:00 PM BST

Welcome to an enlightening journey through the world of MEMS sensors and AI, where we explore the innovative fusion of ST's cutting edge MEMS sensor technology and the power of embedded AI. In this webinar, we will discover the latest innovations in ST MEMS sensors, understand AI integration inside sensors and see how these technologies are being applied in various industries, to improve efficiency, safety, user experience and reduce power consumption of the application.

Future Proof Your ML
Journey to the Edge

Date: Monday, June 24, 2024
1:00 PM - 2:00 PM BST

Billions of devices exist between a PC and smartphone and have the potential to embody AI through an infinite amount of LLMs and LMMs. The opportunity to embed conversational AI throughout these machines will be the biggest technology transformation of our lifetime.

Tiniest Gesture-based Remote Control. Practical session

(Date: Monday, June 03, 2024
1:00 PM - 2:00 PM BST)

TinyML has propelled the field of AI and machine learning into a new era of innovation.

Redefining AI at Edge with Alif

(Date: Monday, May 27, 2024
1:00 PM - 2:00 PM BST)

Alif will enable you to address the need for higher and more efficient compute capacity. Allowing you to anticipate growing applications and use cases in embedded AI systems, while ensuring the there is a viable cost effective path to achieving a truly scalable platform.

Design low-power Linux AI applications using the iMX 93 Apps Processor, featuring the Arm® Ethos-U65 neural processing unit

(Date: Monday, May 20, 2024
2:00 PM - 3:00 PM CEST)

Learn how a Neural Processing Unit (NPU) has a significant impact on the power consumption and performance of your Linux-based artificial intelligence system. NXP will introduce the i.MX 93 processor followed by a demonstration of the Arm Ethos-U65 NPU across a variety of applications. We’ll finish by showing how to evaluate machine learning applications with the i.MX 93 processor. A limited number of i.MX 93 EVK boards will be available free of charge for qualified attendees!

Synap toolkit - speedup AI application development

(Date: Monday, May 13, 2024)

Use Synap toolkit to develop an AI application that monitors heart rate via facial recognition, focusing on speeding up face detection to enable real-time heart rate monitoring.

ST Edge AI Suite, the unified AI toolchain - part 2

(Date: Monday, May 6, 2024)

Tiny Machine Learning and Embedded engineers urge new tools to support them in being faster, more productive to unleash their creativity more than ever. Therefore, ST, devoted its best resources across product divisions and system research to create the Unified AI Core Technology.

ST Edge AI Suite, the unified AI toolchain - part 1

(Date: Monday, April 29, 2024)

Tiny Machine Learning and Embedded engineers urge new tools to support them in being faster, more productive to unleash their creativity more than ever. Therefore, ST, devoted its best resources across product divisions and system research to create the Unified AI Core Technology.

MCX N series: Push the Edge of What's Possible

(Date: Monday, April 22, 2024)

Highly integrated, low-power MCUs to provide the ultimate balance of performance and power consumption. It highlights multi-tasking performance, on-chip accelerators and advanced security.

In-sensor AI capabilities with Neuton.AI's neural networks

(Date: Monday, April 15, 2024)

The embedding of neural networks in sensors has recently gained popularity. But why is there a need to embed neural networks in sensors? Is it even feasible? What are the advantages of embedding neural networks in sensors compared to embedding them directly into microcontrollers?

5 Steps to realise
AI at the Edge

(Date: Monday, April 9, 2024)

Starting the Arrow Edge AI webinar series with an introduction to the 5 stage development flow for AI at the Edge. Data Engineering, Model Training, Model Optimisation, Deployment, Monitoring



5-step Edge AI Development Flow

Data Engineering

Specify, collect, clean and prepare data for model training


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Icon What is it?

  • Data Collection
  • Data Cleaning
  • Data Transformation
  • Data Integration
  • Data labelling

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Model Training

Selecting and training the model


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  • Algorithm and feature selection
  • Selection of model architecture from standard models
  • Train the model
  • Evaluation and testing

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Model Optimization

Simplifying and optimizing the model


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Icon What is it?

  • Optimization model for MCU, MPU devices
  • Quantization, pruning Improvements display, profiling the model
  • Usage of  different optimization stacks  

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Deploying the model onto the HW platform


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Icon What is it?

  • Deployment and verification of model speed, footprint, accuracy
  • Comparison against different suppliers, platforms

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Testing and monitoring


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Icon What is it?

  • Machine Learning Ops and maintenance
  • Model and data drift, accuracy monitoring  

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