Archive for the ‘AI’ Category

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800G: An Inflection Point for Optical Networks

By Samuel Liu, Senior Director, Product Line Management, Marvell

Digital technology has what you could call a real estate problem. Hyperscale data centers now regularly exceed 100,000 square feet in size. Cloud service providers plan to build 50 to 100 edge data centers a year and distributed applications like ChatGPT are further fueling a growth of data traffic between facilities. Similarly, this explosive surge in traffic also means telecommunications carriers need to upgrade their wired and wireless networks, a complex and costly undertaking that will involve new equipment deployment in cities all over the world.

Weaving all of these geographically dispersed facilities into a fast, efficient, scalable and economical infrastructure is now one of the dominant issues for our industry.

Pluggable modules based on coherent digital signal processors (CDSPs) debuted in the last decade to replace transponders and other equipment used to generate DWDM compatible optical signals. These initial modular products didn’t offer the same performance as the incumbent solutions, and could only be deployed in limited use cases. These early modules, with their large form factors, had performance limitations and did not support the required high-density data transmission. Over time, advances in technology optimized the performance of pluggable modules, and CDSP speeds grew from 100 to 200 and 400 Gbps. Continued innovation, and the development of an open ecosystem, helped expand the potential applications.

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Scaling AI Infrastructure with High-Speed Optical Connectivity

By Suhas Nayak, Senior Director of Solutions Marketing, Marvell

In the world of artificial intelligence (AI), where compute performance often steals the spotlight, there’s an unsung hero working tirelessly behind the scenes. It’s something that connects the dots and propels AI platforms to new frontiers. Welcome to the realm of optical connectivity, where data transfer becomes lightning-fast and AI’s true potential is unleashed. But wait, before you dismiss the idea of optical connectivity as just another technical detail, let’s pause and reflect. Think about it: every breakthrough in AI, every mind-bending innovation, is built on the shoulders of data—massive amounts of it. And to keep up with the insatiable appetite of AI workloads, we need more than just raw compute power. We need a seamless, high-speed highway that allows data to flow freely, powering AI platforms to conquer new challenges. 

In this post, I’ll explain the importance of optical connectivity, particularly the role of DSP-based optical connectivity, in driving scalable AI platforms in the cloud. So, buckle up, get ready to embark on a journey where we unlock the true power of AI together. 

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AI and the Tectonic Shift Coming to Data Infrastructure

By Michael Kanellos, Head of Influencer Relations, Marvell

AI’s growth is unprecedented from any angle you look at it. The size of large training models is growing 10x per year. ChatGPT’s 173 million plus users are turning to the website an estimated 60 million times a day (compared to zero the year before.). And daily, people are coming up with new applications and use cases. 

As a result, cloud service providers and others will have to transform their infrastructures in similarly dramatic ways to keep up, says Chris Koopmans, Chief Operations Officer at Marvell in conversation with Futurum’s Daniel Newman during the Six Five Summit on June 8, 2023. 

“We are at the beginning of at least a decade-long trend and a tectonic shift in how data centers are architected and how data centers are built,” he said.  

The transformation is already underway. AI training, and a growing percentage of cloud-based inference, has already shifted from running on two-socket servers based around general processors to systems containing eight more GPUs or TPUs optimized to solve a smaller set of problems more quickly and efficiently.  

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Are We Ready for Large-scale AI Workloads?

By Noam Mizrahi, Executive Vice President, Chief Technology Officer, Marvell

Originally published in Embedded

ChatGPT has fired the world’s imagination about AI. The chatbot can write essays, compose music, and even converse in different languages. If you’ve read any ChatGPT poetry, you can see it doesn’t pass the Turing Test yet, but it’s a huge leap forward from what even experts expected from AI just three months ago. Over one million people became users in the first five days, shattering records for technology adoption.

The groundswell also strengthens arguments that AI will have an outsized impact on how we live—with some predicting AI will contribute significantly to global GDP by 2030 by fine-tuning manufacturing, retail, healthcare, financial systems, security, and other daily processes.

But the sudden success also shines light on AI’s most urgent problem: our computing infrastructure isn’t built to handle the workloads AI will throw at it. The size of AI networks grew by 10x per year over the last 5 years. By 2027 one in five Ethernet switch ports in data centers will be dedicated to AI, ML and accelerated computing.

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Introducing Nova, a 1.6T PAM4 DSP Optimized for High-Performance Fabrics in Next-Generation AI/ML Systems

By Kevin Koski, Product Marketing Director, Marvell

Last week, Marvell introduced Nova™, its latest, fourth generation PAM4 DSP for optical modules. It features breakthrough 200G per lambda optical bandwidth, which enables the module ecosystem to bring to market 1.6 Tbps pluggable modules. You can read more about it in the press release and the product brief.

In this post, I’ll explain why the optical modules enabled by Nova are the optimal solution to high-bandwidth connectivity in artificial intelligence and machine learning systems.

Let’s begin with a look into the architecture of supercomputers, also known as high-performance computing (HPC).

Historically, HPC has been realized using large-scale computer clusters interconnected by high-speed, low-latency communications networks to act as a single computer. Such systems are found in national or university laboratories and are used to simulate complex physics and chemistry to aid groundbreaking research in areas such as nuclear fusion, climate modeling and drug discovery. They consume megawatts of power.

The introduction of graphics processing units (GPUs) has provided a more efficient way to complete specific types of computationally intensive workloads. GPUs allow for the use of massive, multi-core parallel processing, while central processing units (CPUs) execute serial processes within each core. GPUs have both improved HPC performance for scientific research purposes and enabled a machine learning (ML) renaissance of sorts. With these advances, artificial intelligence (AI) is being pursued in earnest.

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