Google engineers have left one of its most secretive AI projects to form a stealth start-up
Several Google engineers have left one of its most
secretive AI projects to form a stealth start-up
Groq founders previously helped create Google's Tensor
Processing Unit.
Venture capitalist Chamath Palihapitiya led a $10 million
investment in the start-up.
The project is in secretive stealth mode.
Ari Levy April 20, 2017 CNBC.com
Google has slowly been pulling back the curtain on homegrown
silicon that could define the future of machine learning and artificial
intelligence.
Some key creators of that project — the Tensor Processing
Unit, or TPU — recently left to team up with Chamath Palihapitiya, one of
Silicon Valley's most prominent and outspoken young venture investors, on a
stealth start-up.
Groq Inc. is the name of the company, at least for the
time being.
There are no promotional materials or website. All that
exists online are a couple SEC filings from October and December showing that
the company raised $10.3 million, and an incorporation filing in the state of
Delaware on Sept. 12.
"We're really excited about Groq," Palihapitiya
wrote in an e-mail. "It's too early to talk specifics, but we think what
they're building could become a fundamental building block for the next
generation of computing."
Groq names three principals in the SEC documents:
Jonathan Ross, who helped invent the TPU, Douglas Wightman, an entrepreneur and
former engineer at the Google X "moonshot factory" and Palihapitiya,
founder of investment firm Social Capital. The listed address is Social
Capital's headquarters.
Palihapitiya told CNBC last month that he invested in a
team of ex-Googlers who helped build the chip, which he first heard about on an
earnings call 2 ½ years ago.
"They randomly mentioned that they built their own
chip for AI and I thought, 'what is going on here, why is Google competing with
Intel?'" Palihapitiya said in an interview on "Squawk Box."
The company (which we now know is Groq) now has eight of
the first 10 people from the TPU team "building a next-generation
chip," he said.
All start-ups are hard, but a new chip company is
something most venture capitalists won't touch. The research and development
costs required to get a working prototype can be exorbitant. Then there's
manufacturing and the Herculean challenge of finding device makers to take a
chance on unproven technology.
Also, the incumbents — Intel, Qualcomm and Nvidia — are
massive, and Google, Apple and Amazon are developing their own silicon.
As crazy as it may be, Palihapitiya is taking the plunge.
This next wave of chip innovation "can empower companies like Facebook and
Amazon, Tesla, the government to do things with machine learning and computers
that nobody could do before," he said in last month's interview.
Ross' LinkedIn page says he left Google in September and
is currently "gainfully unemployed." According to Wightman's profile,
he left the same month but doesn't say where he went. Wightman confirmed the
funding in an e-mail and said, "We're still heads down right now."
Launching the TPU
Google made its first public pronouncement about the TPU
in May with a blog post just ahead of the company's annual developers
conference. Norm Jouppi, one of the project heads, said Google had been using
the technology internally for over a year for things like improving the
relevance of search results and the accuracy of its maps. It's also part of
Google's Cloud Platform in a product called TensorFlow that lets other
companies run machine learning workloads in Google's data centers.
The essence of TPUs is the ability to squeeze heavy and
highly sophisticated computation into less silicon. Machine learning, or the
training of computers to get smarter as data sets increase and without the need
for human interference, is weaving its way into all types of consumer and
business apps. At Google's scale, that work is too taxing for today's
processors.
Palihapitiya said at a conference in January that the
main reason he's bullish on Google as an investment is because "they're an
order of magnitude ahead" of everyone else.
He's not the only investor paying attention. Denny Fish,
a portfolio manager who focuses on technology at Janus, said Google is doing
everything it can to ensure maximum performance and efficiency to handle the
heft of machine learning workloads.
"They've said, if we're going to do this and win, we
need the most efficient way using the least amount of power," he said.
"They feel like they've cracked the code."
Earlier this month, Google provided its first real update
on TPUs in a 17-page report titled, "In-Datacenter performance analysis of
a Tensor Processing Unit."
In a summary blog post, Jouppi wrote that AI workloads
using TPUs are running 15 to 30 times faster than contemporary processors,
while efficiency is 30 to 80 times better. The study compared TPUs to chips
from Intel and Nvidia.
Ross was one of 75 authors of the report. He's also
listed in the paper as an inventor on four patents, all tied to neural networks
processors and computation, which you can think of as computing systems
designed to mimic the brain.
Ross' LinkedIn page says he started the TPU with one
other engineer as part of a 20 percent project, the perk that lets Google employees
spend one-fifth of their time working on a side project they think will benefit
the company.
From 2013 to 2015, he worked as a Google hardware
engineer in Madison, Wisconsin, home to one of the principal hotbeds of TPU
development.
Wightman co-founded four companies before his Google
days. At Google X he worked on futuristic projects like Loon, a network of
balloons that's setting out to provide a more extensive internet.
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