Powering AI
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Figure 1: Typical components on SMCs
STMicroelectronics' new GaN Semiconductors Improve Energy Efficiency for High-Demand Applications from AI Servers to Robotics
Microchip Launches 3.3 kV HV‑D3 mSiC Power Modules to Enable Solid-State Transformers for AI Data Centers
Partha P. Mukherjee, Ph.D., Professor & University Faculty Scholar, School of Mechanical Engineering, and Director, Center for Advances in Resilient Energy Storage (CARES), Purdue University
Derek Matthews, Chief Strategy Officer and Electrical Architect for BaRupOn
Infineon Collaborates with d-Matrix to Optimize Performance and Power Efficiency for Interactive AI Inferencing
Gaurang Shah, Vice President and General Manager, Embedded Processing Product Group
Marc Schütz, Chief Product Officer at Revizto
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Figure 1: Evolution of data center power architecture: (a) today: AC distribution, integrated IT racks; (b,c) next-gen systems: AC distribution, disaggregated IT racks with 50 V servers (b) or 800 V servers (c); (d) future systems: data center-wide HVDC, 800 V servers
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SiTime Boosts GPU Utilization in AI Data Centers with Elite 2 Super-TCXO
Jason Lomberg, North American Editor, PSD
Ally Winning, European Editor, PSD
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Figure 1: An example using the Saf-D-Grid Max connector demonstrates that, at constant ambient temperature, connector temperature increases as applied current increases
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Figure 1: AI scaling laws show that larger models trained with exponentially more compute consistently achieve better performance
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Figure 1: TRIA SM2S-QCS6490 - Compute Module by Tria
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Figure 1: Raspberry Pi Pico Thermal Demo in GPStudio
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Figure 1: The Knowledge Ingestion Pipeline: How LLMs and VLMs absorb expertise from human video demonstrations and technical documents to generate robot task policies
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Figure 1: 48V data centre power delivery to rack
Kevin Parmenter, Pins Out Engineering, for TSC America, Inc.
Jason Lomberg, North American Editor, PSD
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