Groq's website claims that its first chip will run 400 trillion operations per second, more than twice Google’s latest tensor processing unit – more commonly known as the TPU – which supports 180 teraops in the training phase of deep learning. It will perform eight trillion operations per Watt, the website said. [image above by Google]
Groq is tapping into a creative resurgence in the chip industry to make custom server chips for artificial intelligence. Like others, it is attempting to unseat Nvidia, whose graphics chips are currently the “gold standard” for running the intense calculations required to train deep learning software and then make inferences with it.
The start-up, funded with $10.3 million from venture capitalist Chamath Palihapitiya, is staffed with eight of the first 10 members of the team that designed the TPU, including Groq’s founder Jonathan Ross. It also recently hired Xilinx’s vice president of sales Krishna Rangasayee as chief operating officer.
It would be an accomplishment for Groq to reveal its silicon less than two years after it was founded. The company’s chip engineers met similarly tight deadlines at Google, where they taped out the first TPU in only around 14 months. The second generation came out a year later in time for Google’s I/O conference.
Groq is not only battling Nvidia for the hearts and minds of data scientists but also Google, which offers its custom silicon over the cloud. It will also compete with Intel, which plans to release a custom processor before the end of 2017 that provides 55 trillion operations per second for training neural networks.
Every chip company has set its sights on the market position occupied by Nvidia, which has a srong hold on the market for deep learning hardware. Nvidia said, in November 2017, that most major server manufacturers and cloud computing firms were using graphics chips based on its new Volta architecture. (It