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How To Perform Fault Classification With an AI-Based Vibration Sensor Node

How To Perform Fault Classification With an AI-Based Vibration Sensor Node

Learn how to use edge AI technology to do predictive maintenance, leveraging machine learning, vibration sensors, and wireless connectivity.


How Will Angstrom-Scale Chips Advance the Electronics Industry?

How Will Angstrom-Scale Chips Advance the Electronics Industry?

Angstrom-scale ICs will require innovation across the entire semiconductor ecosystem: This will include advances in both the hardware (transistors, power distribution, and connection of multi-die systems) and tools (EDA tools with AI/ML and silicon life-cycle management).


Understanding the Compute Hardware Behind Generative AI

Understanding the Compute Hardware Behind Generative AI

Generative AI tools like ChatGPT have had a huge impact in numerous sectors of society. As engineers, it’s helpful for us to understand the computing technology that makes it possible.


Increasing the Accessibility of Machine Learning at the Edge

Increasing the Accessibility of Machine Learning at the Edge

"Edge intelligence" is becoming more accessible—even to those designers without formal data science training—as new hardware becomes available.


Nine Factors to Consider for Board-Level Machine Vision Camera Integration

Nine Factors to Consider for Board-Level Machine Vision Camera Integration

Leveraging board-level cameras offer a variety of benefits. To help identify the right mix of features and design elements, here are some factors to consider when selecting and designing in an embedded machine vision camera.


Adding an ISP and Machine Learning Acceleration to the i.MX 8M Family

Adding an ISP and Machine Learning Acceleration to the i.MX 8M Family

Learn how the i.MX 8M Plus applications processor enables edge computing, speeding up machine learning for a variety of applications including industrial tasks.


How to Implement Digit Recognition with TensorFlow Lite using an i.MX RT1060 Crossover MCU

How to Implement Digit Recognition with TensorFlow Lite using an i.MX RT1060 Crossover MCU

This article looks at digit detection and recognition using MNIST eIQ as an example, which consists of several parts — the digit recognition is performed by a TensorFlow Lite model, and a GUI is used to increase the usability of the i.MX RT1060 device.


An Example of Securing In-Cabin AI using TEE on a Secure FPGA SoC

An Example of Securing In-Cabin AI using TEE on a Secure FPGA SoC

This article discusses trusted execution environments — already used in a variety of connected devices — by showing how using TEE and an FPGA SoC can work in vehicle in-cabin AI.


The Importance of Reliability Verification in AI/ML Processors

The Importance of Reliability Verification in AI/ML Processors

With the adoption of artificial intelligence and machine learning in a wide variety of applications, reliability verification of AI/ML processors is critical since failures can have major consequences for the validity and legitimacy of AI/ML technology.


Making the Cloud More Powerful: Xilinx FPGAs and Adaptive Workload Acceleration

Making the Cloud More Powerful: Xilinx FPGAs and Adaptive Workload Acceleration

Historically, FPGAs have been challenging to work with. To combat that reputation, Xilinx developed programmable devices that simplify—and accelerate—the implementation of customized hardware development.


Accelerating Embedded Vision Integration with Xilinx SoCs and the reVISION Stack

Accelerating Embedded Vision Integration with Xilinx SoCs and the reVISION Stack

SoCs with programmable logic are an essential element of real-time embedded vision systems. Designers can capitalize on the power and efficiency of Xilinx's Zynq Ultrascale+ MPSoC devices to implement their designs using Avnet's Embedded Vision Kits and the Xilinx reVISION stack.


Intelligence at the Edge Part 2: Reduced Time to Insight

Intelligence at the Edge Part 2: Reduced Time to Insight

In this multipart industrial IoT series, we will break down and explore the fundamental aspects of the edge node interpretation within the larger IoT framework: sensing, measuring, interpreting, and connecting data, with additional consideration for power management and security.


Intelligence at the Edge Part 1: The Edge Node

Intelligence at the Edge Part 1: The Edge Node

The industrial Internet of Things (IoT) encompasses the broad transformation underway that will make pervasive sensing across connected machines not just a competitive advantage, but an essential fundamental service. The industrial IoT starts with the edge node, which is the sensing and measurement entry point of interest.


Developing Smarter, Safer Cars with ADAS (Automotive Advanced Driver Assistance Systems) IP

Developing Smarter, Safer Cars with ADAS (Automotive Advanced Driver Assistance Systems) IP

Optimizing performance, safety, and reliability in ADAS applications via design, verification, and processor IP and a holistic design approach.