Networking vendors are competing in a tight market to produce networking chips that can handle artificial intelligence (AI) and machine learning (ML) workloads. Late last month, Cisco announced its Silicon One G200 and G202 ASICs, pitting it against offerings from Broadcom, NVIDIA, and Marvell.
A recent IDC forecast shows how companies plan to spend more on AI. The research firm predicts that global spending on AI will climb to $154 billion in 2023 and at least $300 billion by 2026. In addition, by 2027, almost 1 in 5 Ethernet switch ports that data centers purchase will be related to AI/ML and accelerated computing, according to a report by research firm 650 Group.
How Cisco Networking Chips Improve Workload Time
Cisco says the Silicon One G200 and G202 ASICs carry out AI and ML tasks with 40% fewer switches at 51.2Tbps. They enable customers to build a 32K 400G GPUs AI/ML cluster on a two-layer network with 50% less optics and 33% less networking layers, according to the company. The G200 and G202 are the fourth generation of the company's Silicon One chips, which are designed to offer unified routing and switching.
"Cisco provides a converged architecture that can be used across routing, switching, and AI/ML networks," Cisco fellow Rakesh Chopra told Network Computing.
The ultralow latency, high performance, and advanced load balancing allow the networking chips to handle AI/ML workloads, according to Chopra. In addition, enhanced Ethernet-based capabilities also make these workloads possible.
“Fully scheduled and enhanced Ethernet are ways to improve the performance of an Ethernet-based network and significantly reduce job completion time,” Chopra said. “With enhanced Ethernet, customers can reduce their job completion time by 1.57x, making their AI/ML jobs complete quicker and with less power.”
Cisco says the G200 and G202 also incorporate load balancing, better fault isolation, and a fully shared buffer, which allow a network to support optical performance for AI/ML workloads.
How Chipmakers Are Tackling AI
Networking vendors are rolling out networking chips with higher bandwidth and radix, which are the number of devices in which they can connect to be able to carry out AI tasks, according to Chopra. They are also enabling GPUs to communicate without interference, eliminating bottlenecks for AI/ML workloads, he said.
