Nvidia is a major player in artificial intelligence hardware, and it showed off some of its latest advancements in chip-to-chip communications in an area that, if history is any guide, will greatly increase the speed and size of AI systems.
The company’s new inventions center on improving the speed and efficiency with which data information is passed from one chip to another, a key bottleneck in building more powerful and complex A.I. models.
This push is driven by the development of Nvidia’s high-bandwidth interconnect technology. Expanding on their current NVLink, the company is bringing new architectures and protocols to the table that are creating what they describe as a massive increase the data throughput and decrease the latency between GPUs, CPUs and dedicated AI accelerators.
Such accelerated communication fabric is essential for distributed training and inference workloads that require large data exchanges between processing units at high speed.
Nvidia’s game planOne big part of Nvidia’s strategy is to create interoperability in the AI hardware space. The company said that it will now be licensing some of its interconnect technology out to other chip makers, a decision that might result in a greater variety of and more optimized AI system designs.
By providing a bridge to enable Nvidia’s high-capacity GPUs and other silicon to communicate more easily, the effort expected to unleash unmatched performance and flexibility for AI developers.
Better chip-to-chip communication means a number of benefits for AI applications. Quicker data transfer does train big AI models faster, though, saving time and resources.
Faster response times for parallel processing means that more advanced AI tasks can be taken on and more complex algorithms can be used. What’s more, it’s getting easier to build bigger, more scalable AI infrastructure in datacenters.
Analysts say Nvidia’s push to develop more sophisticated chip connectivity is just another indication the company is “trying to become the Intel of the AI era.
The company is attacking a core problem in AI hardware in order to unlock the creation of next-generation AI applications across industries – including autonomous vehicles, healthcare, natural language processing, and scientific research.
Chip communication will become a decisive factor to achieve the full potential of these blooming (but hungry) AI models as their size increases.