Broadcom's on-chip neural-network inference engine to improve n/w performance of switch

Broadcom has integrated a new on-chip neural-network inference engine called NetGNT in its latest software-programmable Trident 5-X12 chip for network switch. This engine handles AI/ML load triggered traffic congestion such as “incast” where multiple packet flow and converge on the same port and buffer at roughly the same time causing congestion. NetGNT recognize such patterns in real-time and enable congestion-control techniques and up the network performance. This ML inference engine runs in hardware and can be trained dynamically to look for different types of traffic patterns that span the entire chip without affecting throughput and latency. NetGNT works in parallel augmenting the standard packet-processing pipeline. “We continue to push the envelope and introduce brand-new technologies such as NetGNT to the market. We also listen very closely to our customers who have made it clear that one size does not fit all. They rely on us to deliver a broad portfolio of chips, customized for different applications,” said Ram Velaga, senior vice president and general manager, Core Switching Group, Broadcom. “Trident 5-X12 is the most power-efficient ToR (Top of Rack) on the market, while still adding cutting-edge new features that our customers have come to expect from an innovator such as Broadcom.” The software-programmable and field-upgradable Trident 5-X12 uses...