someone will come up with a cure for
cancer using the tech we’re creating.”
“We’re building the motors of
artificial intelligence, really,” says
Knowles. “And what people will build
out of those motors is far greater than
our motors. We want to be the Rolls-
Royce jet engines of AI machinery.”
In essence, the problem Graphcore is
solving is that previous generations of
microprocessors – central processing
and graphics processing units – weren’t
designed for machine intelligence, which
requires a new way of processing data.
Knowles holds up a Graphcore chip.
The size of a small cracker with a dark
grey, metallic centre, it contains 23.6
billion transistor devices all connected
by several kilometres of wiring. As
transistors were progressively shrunk
over the decades so that more of them
could fit on to each chip, the chips
themselves grew correspondingly hotter
as energy demands increased. “We’re
almost at the end of that gravy train
now,” says Knowles. “The objective of
chip design always used to be to go as
fast as possible; now it’s to make the
most use of the energy available.”
“To make them as efficient as
possible,” clarifies Toon. “Exactly,”
says Knowles. “And actually, you design
things in a completely different way if
you’re most interested in energy, and
less interested in speed per se. So why do
we want more computing performance?
We have just started to work out how to
mechanise intelligence. And what do we
mean by intelligence? A machine that
can learn by its experience, or by being
given examples, or by itself, discov-
ering things. In no sense, historically,
has a computer solved a problem – it
was always the person who wrote the
program. AI flips that on its head.”
Suddenly, because of the AI workload,
demand for processing power has surged- at just the moment when traditional
methods of making chips do more work
are no longer up to the job. “Explaining to
a computer how to learn is quite different
to explaining to it how to do traditional
supercomputer maths, for example,”
he adds. “So we’ve set about trying to
solve those two problems – intelligence
is a different workload, and focusing
on efficiency, not speed – with our IPU.”
Whereas other AI hardware
companies have focused on neural
networks – a type of knowledge model
for capturing the sort of intelligence in
the human cortex, which is essentially
designed to recognise numerical
patterns – Graphcore has built an
architecture that is more flexible. It
can run current machine-learning
approaches, as well as new and emerging
Below: Graphcore co-founders Simon
Knowles (left) and Nigel Toon: “We want
to be the Rolls-Royce of AI machinery”15209-19-FTgraphcore.indd 152 19/07/2019 17:07