Author

Aharon Etengoff

5 articles

What type of interconnects and connectors link accelerator cards in AI data centers?

Aharon Etengoff May 13, 2026

Many data centers are packed with racks of high-performance graphics processing units (GPUs) and tensor processing units (TPUs). These accelerators process massive artificial intelligence (AI) and machine learning (ML) datasets, executing complex operations in parallel and exchanging data at high speed. This article explores the interconnects and connectors that link AI accelerator clusters together.

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What is the role of liquid cooling connectors in AI data centers?

Aharon Etengoff May 13, 2026

Artificial intelligence (AI) and machine learning (ML) applications consume significant power and generate considerable heat in data centers.

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Beyond SDVs: how AI optimizes electric vehicles

Aharon Etengoff June 17, 2026

Many automotive manufacturers classify new cars and trucks as software-defined vehicles (SDVs). As SDVs by design, electric vehicles (EVs) optimize vital systems and functions with sophisticated artificial intelligence (AI) and machine learning (ML) capabilities. This article discusses AI’s crucial role in EVs, from smart charging and advanced driver assistance systems (ADAS) to predictive maintenance and…

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Accelerating high-performance AI workloads with photonic chips

Aharon Etengoff May 13, 2026

Artificial intelligence (AI) and machine learning (ML) continue to push the limits of conventional semiconductor architectures. To increase speeds, lower latency, and optimize power consumption for high-performance workloads, semiconductor companies, and research institutions are developing advanced photonic chips that operate on the principles of light rather than electrical currents.

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How to minimize design cycles for AI accelerators with advanced DFT and silicon bring-up

Aharon Etengoff May 13, 2026

Design for testability (DFT) embeds testable features into an integrated circuit (IC) during design, while silicon bring-up initiates chip evaluation and debugging. Streamlining these sequential processes minimizes design cycles and shortens time-to-market (TTM) for advanced artificial intelligence (AI) accelerators.

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