Cisco: LPO not a panacea but plays strategic role in AI networks

Cisco has committed to developing Linear Pluggable Optics (LPO) options for its Silicon One family to address the growing need for lower power, high-throughput networking for AI.
Cisco recently ratcheted up the speed of its Silicon One portfolio with the introduction of a 102.4 Terabit/sec G300 chip and switch family. As part of that launch, Cisco rolled out its first commercially available 800G LPO modules for its Nexus 9000 series switches and 8000 series routers. Outfitted with the LPO module, these networking boxes are aimed at AI leaf-spine fabrics, data center interconnects, and other high density 800G deployments, according to Cisco.
Supporting 800G LPO on Nexus 9000 and 8000 series platforms takes advantage of the power of the Silicon One architecture while addressing the energy savings and network performance needs that hyperscalers or other high-end users need now, experts say. 
“For us, there are going to be niche, targeted applications for 800G LPO, in my view, and customers seeking to reduce power will take advantage of it, but it’s not a panacea,” Bill Gartner, senior vice president and general manager of Cisco’s optical systems and optics business, told Network World. 

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In other words, it’s not fully plug-and-play like some non-LPO optics, Gartner said. “We can’t make a claim that you can take our 800G LPO, or anybody else’s, and just plug it into anybody’s host, and it’s going to work. It’s going to require some pairwise testing, and it has to be paired with an LPO optic on the other end of the link as well,” Gartner said. Not all silicon is capable of doing this reliably, and that’s why LPO deployments need thorough validation between the optic and the host platform.

 

Automation can benefit professionals across various fields, including project managers, analysts, and solo founders. We all face repetitive digital tasks that consume our time, such as gathering data from the web, cleaning and standardizing it, updating spreadsheets, and creating clear, actionable reports. 

Become a data-savvy business leader

5 Workflow Automation Tools for All Professionals

Five powerful automation tools to help you streamline repetitive digital tasks, increase productivity, and create smarter workflows without requiring deep technical skills.

By Abid Ali Awan, KDnuggets Assistant Editor on December 16, 2025 in Artificial Intelligence

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# Introduction

 
Automation can benefit professionals across various fields, including project managers, analysts, and solo founders. We all face repetitive digital tasks that consume our time, such as gathering data from the web, cleaning and standardizing it, updating spreadsheets, and creating clear, actionable reports. 

In this article, we will explore five powerful yet user-friendly workflow automation tools. With features like drag-and-drop nodes, prebuilt connectors, and guided templates, you can easily create end-to-end workflows without the need for extensive engineering knowledge. 

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Quantum computing leap for remote energy systems

 

Engineering researchers at Flinders University have won federal funding to develop a quantum computing-based demonstrator for remote community energy systems.

A quantum computer can perform calculations in minutes that take a supercomputer days, consuming kilowatts instead of megawatts of power.

Led by Professor of Electronic and Electrical Engineering Apel Mahmud the project will use advanced quantum optimisation and machine learning algorithms to design a quantum-based demonstrator model for remote community energy systems.

The $1.45 million project, kickstarted by $1.15 million in the latest round of grants announced by the Australian Government’s Critical Technologies Challenge Program, will work with industry partners EfficientSee and Zeco Australian Energy Solutions, as well as experts from two other Australian universities.

Professor Mahmud’s latest research focuses on sustainable and reliable energy supply solutions for rural and remote communities, including First Nations communities.

“Building on our feasibility study in Stage 1, the new energy management system will address complex computational challenges associated with reliable electricity supply to remote areas,” Professor Mahmud said.

“More intelligent control and forecasting of supply to these remote energy systems will support government efforts to reduce the financial burden of these high-cost systems.”

Key outcomes of the project will include creating a digital twin of remote energy systems and integrating these into a working prototype.

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Neural CAD accelerates design exploration

 

In the early stages of a design project, during ideation and conceptualisation, tools should accommodate the broadest methods of expression. Early specifications, outlines, rough sketches and photos (among others) are all essential in expressing ideas.

Yet, the core paradigm of CAD software, parametric CAD, has remained largely unchanged for 40 years. These engines, built using traditional software technology, rely on a keyboard, mouse and typical user interface. Creators must explicitly define parameters such as the dimensions of an object. That can restrict creativity.

Creators need technology that doesn’t obstruct their creative flow. Instead, it should enable rapid and iterative exploration of ideas, regardless of how detailed or approximate they might be.

Over the past decade, research has underscored that early-stage explorations significantly enhance project metrics like cost, sustainability and suitability. That’s why recent advancements in neural technology are generating such excitement.

Unlike parametric CAD, they are learning and improving, so that they constantly align better to creators’ needs and ways of working. Meanwhile, they can reliably reason about the three-dimension and physical world.

Ultimately, these advancements are aimed at making creators faster, smarter and more competitive. The quicker designers can turn around their designs, the faster they can move products to markets – but the impact goes beyond speed. AI-powered design tools can directly reason about the immense complexity within design creation. It is this that sets the stage for vast improvements across the entire design and make category.

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No wires, no plugs, just power through the air: How Finland is experimenting with wireless electricity

 

Finland is pioneering next-generation wireless electricity systems that transmit power safely through the air without cables or plugs. Researchers and technology companies are testing advanced radio frequency and resonant inductive coupling solutions to energize IoT devices, sensors, and smart infrastructure. These experiments aim to improve efficiency, reduce wiring costs, and enable seamless energy access in homes, factories, and smart cities. By integrating renewable energy and intelligent grid management, Finland is shaping a future where power flows invisibly, reliably, and sustainably across connected environments.

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multi-agent AI is transforming network fault repair

 

The ‘Multi-level multi-agent network fault healing’ Catalyst delivers a hierarchical multi-agent system that uses LLM-driven diagnosis, digital-twin simulation and cross-domain coordination to automate end-to-end network fault detection, analysis and repair.

Large 5G rollouts and the growth of computing-network services have created dense layers of operational systems that generate vast volumes of alarms and events. These environments now demand response times that conventional tools cannot provide. CSPs face surges in work orders triggered by alarm storms, limited coordination between domains, and manual processes that slow fault handling. Diagnosis often relies on isolated tools and human knowledge, with little ability to form a complete cross-domain view. As a result, network operations centers experience long mean time to repair, inconsistent practices, and rising operational costs.

This pressure is particularly visible in transport networks, where faults often involve multiple layers and vendors. A single issue can trigger related alarms across fiber, optical, IP and service layers, yet existing systems treat these fragments separately. CSPs cannot easily connect symptoms to a root cause or confirm the impact on services. Manual effort still dominates routine faults, and repetitive tasks consume valuable specialist time. These challenges impose commercial strain. Slow diagnosis prolongs service interruptions and weakens SLA performance. High OPEX limits the ability to scale new services. As networks evolve toward Level 4 autonomy, these constraints become incompatible with the operational maturity required.

The market context also shows clear demand for more intelligent and automated models. CSPs need systems that integrate network insights, interpret intent, act across domains, and verify change before execution. They require architectures that support collaboration rather than isolated automation. Crucially, they need solutions that can be deployed at scale without heavy customization, and with standardized interfaces that avoid vendor lock-in.

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A Radical New Computer Could Replace Electricity With Light—and Make Processing Unstoppable

 

In the new research, scientists describe their desire to use photons for tensor processing—a form of computer math where entire sets of data are digested together instead of just one datum at a time. This is a paradigm for data crunching in which numbers and other values can be stored across many dimensions in a large math structure. This type of math has potential value in many fields, but for machine learning structures like large language models (LLMs) and other forms of artificial intelligence (AI), it’s considered a gamechanger because it’s very compatible with model training and other AI work. 

Inside your traditional computer, everything you see, type, and click on is a high level representation of code. Your hardware has to be ready for you to do a variety of tasks as a general user, from crunching a huge Excel spreadsheet into a marketing report to playing a cutting-edge video game. But AI often does not need breadth in that way. Its hardware can focus on specific types of math and storage that are the most helpful and efficient, and transitioning these processes from electron to photon computing could be even faster.

In their paper, the scientists in China lay out a new photon process where just one light source can do more than one tensor operation at once. This is essential, because this math involves multiplying huge grids of values into other huge grids of values, typically resulting in a solution represented by an even larger grid. Inside the computer, if one math problem can’t run in tandem with other operations, the system can get really bogged down and slow.

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