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.