Archive for the 'Server Connectivity' Category
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October 29, 2024
Nine Things to Remember About the Future of Copper in Computing
By Michael Kanellos, Head of Influencer Relations, Marvell and Vienna Alexander, Marketing Content Intern, Marvell
Is copper dead?
Not by a long shot. Copper technology, however, will undergo a dramatic transformation over the next several years. Here’s a guide.
1. Copper is the Goldilocks Metal
Copper has been a staple ingredient for interconnects since the days of Colossus and ENIAC. It is a superior conductor, costs far less than gold or silver and offers relatively low resistance. Copper also replaced aluminum for connecting transistors inside of chips in the late 90s because its 40% lower resistance improved performance by 15%1.
Copper is also simple, reliable and hearty. Interconnects are essentially wires. By contrast, optical interconnects require a host of components such as optical DSPs, transimpedance amplifiers and lasers.
“The first rule in optical technology is ‘Whatever you can do in copper, do in copper,’” says Dr. Loi Nguyen, EVP of optical technology at Marvell.
2. But It’s Still a Metal
Nonetheless, electrical resistance exists. As bandwidth and network speeds increase, so do heat and power consumption. Additionally, increasing bandwidth reduces the reach, so doubling the data rate reduces distance by roughly 30–50% (see below).
As a result, optical technologies have replaced copper in interconnects five meters or longer in data centers and telecommunication networks.
Source: Marvell
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September 25, 2024
Marvell COLORZ 800 Named Most Innovative Product at ECOC 2024
By Michael Kanellos, Head of Influencer Relations, Marvell
With AI computing and cloud data centers requiring unprecedented levels of performance and power, Marvell is leading the way with transformative optical interconnect solutions for accelerated infrastructure to meet the rising demand for network bandwidth.
At the ECOC 2024 Exhibition Industry Awards event, Marvell received the Most Innovative Pluggable Transceiver/Co-Packaged Module Award for the Marvell® COLORZ® 800 family. Launched in 2020 for ECOC’s 25th anniversary, the ECOC Exhibition Industry Awards spotlight innovation in optical communications, transport, and photonic technologies. This recognition highlights the company’s innovations in ZR/ZR+ technology for accelerated infrastructure and demonstrates its critical role in driving cloud and AI workloads.
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September 22, 2024
Five Things to Know About the Future of Long Distance Optics
By Michael Kanellos, Head of Influencer Relations, Marvell
Coherent optical digital signal processors (DSPs) are the long-haul truckers of the communications world. The chips are essential ingredients in the 600+ subsea Internet cables that crisscross the oceans (see map here) and the extended geographic links weaving together telecommunications networks and clouds.
One of the most critical trends for long-distancer communications has been the shift from large, rack-scale transport equipment boxes running on embedded DSPs often from the same vendor to pluggable modules based on standardized form factors running DSPs from silicon suppliers tuned to the power limits of modules.
With the advent of 800G ZR/ZR+ modules, the market arrives at another turning point. Here’s what you need to know.
It’s the Magic of Modularity
PCs, smartphones, solar panels and other technologies that experienced rapid adoption had one thing in common: general agreement on the key ingredients. By building products around select components, accepted standards and modular form factors, an ecosystem of suppliers sprouted. And for customers that meant fewer shortages, lower prices and accelerated innovation.
The same holds true of pluggable coherent modules. 100 Gbps coherent modules based on the ZR specification debuted in 2017. The modules could deliver data approximately 80 kilometers and consumed approximately 4.5 watts per 100G of data delivered. Microsoft became an early adopter and used the modules to build a mesh of metro data centers1.
Flash forward to 2020. Power per 100G dropped to 4W and distance exploded: 120k connections became possible with modules based on the ZR standard and 400k with the ZR+ standard. (An organization called OIF maintains the ZR standard. ZR+ is controlled by OpenROADM. Module makers often make both varieties. The main difference between the two is the amplifier: the DSPs, number of channels and form factors are the same.) ®
The market responded. 400ZR/ZR+ became adopted more rapidly than any other technology in optical history, according to Cignal AI principal analyst Scott Wilkinson.
“It opened the floodgates to what you could do with coherent technology if you put it in the right form factor,” he said during a recent webinar.
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June 11, 2024
How AI Will Drive Cloud Switch Innovation
This article is part five in a series on talks delivered at Accelerated Infrastructure for the AI Era, a one-day symposium held by Marvell in April 2024.
AI has fundamentally changed the network switching landscape. AI requirements are driving foundational shifts in the industry roadmap, expanding the use cases for cloud switching semiconductors and creating opportunities to redefine the terrain.
Here’s how AI will drive cloud switching innovation.
A changing network requires a change in scale
In a modern cloud data center, the compute servers are connected to themselves and the internet through a network of high-bandwidth switches. The approach is like that of the internet itself, allowing operators to build a network of any size while mixing and matching products from various vendors to create a network architecture specific to their needs.
Such a high-bandwidth switching network is critical for AI applications, and a higher-performing network can lead to a more profitable deployment.
However, expanding and extending the general-purpose cloud network to AI isn’t quite as simple as just adding more building blocks. In the world of general-purpose computing, a single workload or more can fit on a single server CPU. In contrast, AI’s large datasets don’t fit on a single processor, whether it’s a CPU, GPU or other accelerated compute device (XPU), making it necessary to distribute the workload across multiple processors. These accelerated processors must function as a single computing element.
AI requires accelerated infrastructure to split workloads across many processors.
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June 06, 2024
Silicon Photonics Comes of Age
This article is part four in a series on talks delivered at Accelerated Infrastructure for the AI Era, a one-day symposium held by Marvell in April 2024.
Silicon photonics—the technology of manufacturing the hundreds of components required for optical communications with CMOS processes—has been employed to produce coherent optical modules for metro and long-distance communications for years. The increasing bandwidth demands brought on by AI are now opening the door for silicon photonics to come inside data centers to enhance their economics and capabilities.
What’s inside an optical module?
As the previous posts in this series noted, critical semiconductors like digital signal processors (DSPs), transimpedance amplifiers (TIAs) and drivers for producing optical modules have steadily improved in terms of performance and efficiency with each new generation of chips thanks to Moore’s Law and other factors.
The same is not true for optics. Modulators, multiplexers, lenses, waveguides and other devices for managing light impulses have historically been delivered as discrete components.
“Optics pretty much uses piece parts,” said Loi Nguyen, executive vice president and general manager of cloud optics at Marvell. “It is very hard to scale.”
Lasers have been particularly challenging with module developers forced to choose between a wide variety of technologies. Electro-absorption-modulated (EML) lasers are currently the only commercially viable option capable of meeting the 200G per second speed necessary to support AI models. Often used for longer links, EML is the laser of choice for 1.6T optical modules. Not only is fab capacity for EML lasers constrained, but they are also incredibly expensive. Together, these factors make it difficult to scale at the rate needed for AI.
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