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I pointed out recently that although
La La Land is a romance, the movie
opens with cars. The semiconductor
industry is like that, too—no matter
which way you turn it is automotive.
It may not show yet in manufacturing
volume and revenue, since it is
about 10% of the market. However,
the newer parts of automotive,
those associated with autonomous
driving, have ~30% growth rates
(which is close to doubling every
two years, by the rule of 70). There
are several really big changes, such
as automotive Ethernet or security,
which I won't discuss today. But
probably the biggest change is the
need for vision processing.
There are two separate reasons that
this is such a big change. Firstly,
vision processing has to be done
on-vehicle. The amounts of data
are insanely large, too large to
upload to the cloud for processing.
But more fundamentally, a vehicle
cannot require network connectivity
to decide whether a light is green
or red, or whether that thing ahead
is a pedestrian or a mailbox. This
is a level of computation that cars
have never required before, so
is a challenge for the automotive
semiconductor ecosystem. The
traditional suppliers don't understand
high-performance processors and
leading-edge processes. The mobile
semiconductor ecosystem does, but
it doesn't understand automotive
reliability and only recently heard the
magic number 26262.
(For more on ISO 26262, see my
recent post "The Safest Train Is One
That Never Leaves the Station".
For an introduction to convolutional
neural nets (CNN), see Why is Google
So Good at Recognizing Cats?. Also,
last year Cadence ran a seminar in
Vegas that I wrote up in a full week of
posts here, starting on Monday with
Power Efficient Recognition Systems
for Embedded Applications.)
The second change is with vision
processing itself. If you go back only
a few years, vision processing was
algorithmic, with the focus of research
on edge-detection algorithms,
building 3D models from 2D data,
and so on. Now the whole field has
switched to convolutional neural
nets (CNN). But it is not just vision
processing that has gone neural, a
lot of the decision processing has,
too. Arguably, vision processing has
advanced more in the last two to
three years then since...cue dramatic
music...the dawn of time.
Embedded Vision Summit
embedded vision summit badgeToday
Vision C5 DSP for Standalone Neural Network
Processing
Paul McLellan, Cadence
Embedded Solutions
Special Edition
58 l New-Tech Magazine Europe