Israeli AI chip startup Habana Labs Ltd. today unveiled the Habana Gaudi AI Training Processor for data centers.
Habana's first AI Gaudi chip was launched last November and was designed for AI inference applications in edge devices.
Training systems based on Gaudi processors will deliver an increase in throughput of up to four times over systems built with equivalent number GPUs. Gaudi’s innovative architecture enables near-linear scaling of training systems performance, as high throughput is maintained even at smaller batch sizes, thus allowing performance scaling of Gaudi-based systems from a single-device to large systems built with hundreds of Gaudi processors.
The man behind the founding of Habana is Avigdor Willenz, the founder of chipmaker Galileo, which was sold to Marvell in 2000 for $2.7 billion. He serves as Habana's chairman together with cofounders CEO David Dahan and VP development Ran Halutz (the son of
Dahan said, “Training AI models require exponentially higher compute every year, so it’s essential to address the urgent needs of the datacenter and cloud for radically improved productivity and scalability. With Gaudi’s innovative architecture, Habana delivers the industry’s highest performance while integrating standards-based Ethernet connectivity that enables unlimited scale. Gaudi will disrupt the status quo of the AI Training processor landscape.”
“Facebook is seeking to provide open platforms for innovation around which our industry can converge,” said Vijay Rao, Director of Technology, Strategy at Facebook. “We are pleased that the Habana Goya AI inference processor has implemented and open-sourced the backend for the Glow machine learning compiler and that the Habana Gaudi AI training processor is supporting the OCP Accelerator Module (OAM) specification.”
Habana will be sampling the Gaudi to select customers in the second half of 2019.
Since its inception, the company has raised $120 million.
Published by Globes, Israel business news - en.globes.co.il - on June 17, 2019
© Copyright of Globes Publisher Itonut (1983) Ltd. 2019