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Beyond Copper and Optical, a New Interconnect Eyes Next Gen Data Centers

Beyond Copper and Optical, a New Interconnect Eyes Next Gen Data Centers

Both copper and optical interconnects face limitations as choices for next gen data centers. Learn how a third option promises to enable scaling up AI clusters in data centers for years to come.


AI Inferencing in Data Centers: Breaking the Efficiency-Cost Tradeoff

AI Inferencing in Data Centers: Breaking the Efficiency-Cost Tradeoff

Training and inferencing comprise two crucial aspects of AI processing in datacenters. Learn the differences between the two, and the cost-efficiency issues involved.


Post-Quantum Cryptography—Securing Semiconductors in a Post-Quantum World

Post-Quantum Cryptography—Securing Semiconductors in a Post-Quantum World

Quantum computing advances are exciting, but they’re also a looming threat to securing ICs, driving the need for Post-Quantum Cryptography (PQC). Learn about PQC, how it’s being implemented, and the legislation involved.


Harnessing the Power of a Software-Based Image Signal Processing Approach

Harnessing the Power of a Software-Based Image Signal Processing Approach

There are numerous advantages to leveraging a software-based image signal processor (ISP) approach. Learn the advantages and the solutions available to implement this technology.


Edge AI Demands Call For Optimized Storage Controller Chips

Edge AI Demands Call For Optimized Storage Controller Chips

AI will push the limits of PCs and smartphones. In turn, demands on storage controller chips will be intense. Learn how chip architectures and firmware schemes must be optimized for these AI workloads.


Rethinking MCU Architectures for the AI Era

Rethinking MCU Architectures for the AI Era

MCUs that only do control tasks are one thing. But in today’s AI age, a truly AI-enabled MCU needs to offer more. Learn the important factors—from optimized neural processing units (NPUs) to power efficient architectures to clever memory topologies.


Crafting a Silicon Lifecycle Management Strategy for HPC and Data Centers

Crafting a Silicon Lifecycle Management Strategy for HPC and Data Centers

As data center computing and HPC advances, the stakes for ensuring reliability are high. Learn how to develop a silicon lifecycle management (SLM) strategy that ensures a successful future for your designs.


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.


Improving Battery Life in IoT Smart Camera Designs

Improving Battery Life in IoT Smart Camera Designs

The wireless connectivity side eats up a huge portion of the power budget in an IoT smart home security camera design. Learn how to keep power low as smart cameras add more processing intelligence.


System Challenges of Generative AI Inference Acceleration

System Challenges of Generative AI Inference Acceleration

When you look under the hood of generative AI processing, the system design challenges are many. Learn how efficiency, power consumption, and memory issues all come into play.


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.


Overcome Smart Home Technology Limits Using Sensors and Edge Computing

Overcome Smart Home Technology Limits Using Sensors and Edge Computing

Designing smart home devices involves numerous challenges. In this article, learn the important limitations of today’s smart home technologies and how sensor fusion helps smooth the way.


Considerations for Choosing Edge ML Application Hardware

Considerations for Choosing Edge ML Application Hardware

As edge machine learning (ML) applications keep growing, EEs need to understand ML at the edge, especially concerning processing and processing hardware.


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.


Minimizing Stepper Motor Noise and Vibration in Precision Motion Control Applications

Minimizing Stepper Motor Noise and Vibration in Precision Motion Control Applications

Stepper motors work well in a wide range of applications but can struggle with torque ripple and current distortion issues. Learn about QuietStep, a proprietary algorithm from Allegro MicroSystems, as a possible solution. 


Efficient Orthogonal Variable Optimization Algorithm for Communication Systems

Efficient Orthogonal Variable Optimization Algorithm for Communication Systems

This article discusses an algorithm to find the optimum adjusted point in a two-dimensional space with orthogonal input vectors. The algorithm solves equations for intersecting circles based on measured data points.


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.


Machine Learning and Intelligent Vision for the Industrial Edge

Machine Learning and Intelligent Vision for the Industrial Edge

NXP’s i.MX 8M Plus applications processor enables machine learning and intelligent vision for consumer applications and the industrial edge. Learn about the features of this processor and how it can be used in embedded vision systems.