University of Arizona Researchers Develop Optical Switch to Cut Data Center Energy Use
Researchers at University of Arizona have developed a new optical switching technology designed to reduce energy consumption in data centers, according to a press release. The PQT-HOS optical switch is reported to be 1,000 times faster than traditional electrical switches while using only one-thousandth of the energy.
Developed by research professor Pierre-Alexandre Blanche, the PQT-HOS uses light diffraction to maintain data in optical format throughout processing. This design allows for faster data transfer and significantly lower power requirements. The technology has been patented and verified by Microsoft Corporation, UC Berkeley, and Texas Instruments.
The PQT-HOS has been peer reviewed through the University of Arizona College of Optics and is described as ready for commercial prototype development. The university expects the technology to support operators of large data centers by improving efficiency and reducing environmental impact.
We hope you enjoyed this article
Consider subscribing to one of our newsletters like Silicon Brief or Daily AI Brief.
Also, consider following us on social media:
More from Data Centers
Oct 16 EverWind Plans 420 Megawatt AI Data Centre in Nova Scotia Oct 16 Gartner Says $3.5 Trillion in AI Infrastructure Spending Faces Impairment Risk by 2030 Oct 16 KBRA Rated Anthropic BB+ Before $35 Billion Chip Financing Oct 16 China Proposes Export Controls for Advanced AI Models and Chips Oct 16 Hitachi Energy and Ansaldo Energia Partner on Data Center Power SystemsSilicon Brief
Weekly coverage of AI hardware developments including chips, GPUs, cloud platforms, and data center technology.
Whitepaper
Tensordyne Napier: What If One Rack Could Do the Work of Nine?
Tensordyne
This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.
Read moreYou may also like
Salience Labs raises $125 million for optical AI data center switches
Echola Launches 800G and 1.6T Optical Switches for AI Network Testing
CyberTAN unveils 102.4 Tbps liquid cooled Ethernet switch blueprint
Huawei Proposes Next Generation OTN for AI Data Center Networks
Cisco and NVIDIA Plan Open 400G Optics Specification
Daily AI Brief: the AI news that matters, in your inbox.