This technology reduces latency bottlenecks in data center network communication by shifting aggregation processes to programmable switches. iSwitch is particularly relevant to AI applications such as reinforcement learning (RL)-based training, where frequent gradient aggregation typically requires a large number of network hops. In demonstrations, the technology achieved a system-level speedup of more than 3.5x for both synchronous and asynchronous RL distributed training and also improved scalability.

Application

Networking datacenters

Benefits

iSwitch results in a system-level speedup of more than 3.5x for both synchronous and asynchronous RL distributed training and also improves scalability.