Artificial
intelligence is coming to your phone. The iPhone X has a Neural Engine as part
of its A11 Bionic chip; the Huawei Kiri 970 chip has what’s called a Neural
Processing Unit or NPU on it; and the Pixel 2 has a secret AI-powered imaging
chip that just got activated. So what exactly are these next-gen chips designed
to do?
As mobile
chipsets have grown smaller and more sophisticated, they’ve started to take on
more jobs and more different kinds of jobs. Case in point, integrated
graphics—GPUs now sit alongside CPUs at the heart of high-end smartphones,
handling all the heavy lifting for the visuals so the main processor can take a
breather or get busy with something else.
The new breed of
AI chips are very similar—only this time the designated tasks are recognizing
pictures of your pets rather than rendering photo-realistic FPS backgrounds.
What we talk about when we talk about AI
AI, or
artificial intelligence, means just that. The scope of the term tends to shift
and evolve over time, but broadly speaking it’s anything where a machine can
show human-style thought and reasoning.
A person hidden
behind a screen operating levers on a mechanical robot is artificial
intelligence in the broadest sense—of course today’s AI is way beyond that, but
having a programmer code responses into a computer system is just a more
advanced version of getting the same end result (a robot that acts like a
human).
As for computer
science and the smartphones in your pocket, here AI tends to be more narrowly
defined. In particular it usually involves machine learning, the ability for a
system to learn outside of its original programming, and deep learning, which
is a type of machine learning that tries to mimic the human brain with many
layers of computation. Those layers are called neural networks, based on the neural
networks inside our heads.
So machine
learning might be able to spot a spam message in your inbox based on spam it’s
seen before, even if the characteristics of the incoming email weren’t
originally coded into the filter—it’s learned what spam email is.
Deep learning is
very similar, just more advanced and nuanced, and better at certain tasks,
especially in computer vision—the “deep” bit means a whole lot more data, more
layers, and smarter weighting. The most well-known example is being able to
recognize what a dog looks like from a million pictures of dogs.
Plain old
machine learning could do the same image recognition task, but it would take
longer, need more manual coding, and not be as accurate, especially as the
variety of images increased. With the help of today’s superpowered hardware,
deep learning (a particular approach to machine learning, remember), is much
better at the job.
To put it
another way, a machine learning system would have to be told that cats had
whiskers to be able to recognize cats. A deep learning system would work out
that cats had whiskers on its own.
Bear in mind
that an AI expert could write a volume of books on the concepts we’ve just
covered in a couple of paragraphs, so we’ve had to simplify it, but those are
the basic ideas you need to know.
AI chips on smartphones
As we said at
the start, in essence, AI chips are doing exactly what GPU chips do, only for
artificial intelligence rather than graphics—offering a separate space where
calculations particularly important for machine learning and deep learning can
be carried out. As with GPUs and 3D graphics, AI chips give the CPU time to
focus on other tasks, and reduces battery draw at the same time. In also means
your data is more secure, because less of it has to be sent off to the cloud
for processing.
So what does
this mean in the real world? It means image recognition and processing could be
a lot faster. For instance, Huawei claims that its NPU can perform image
recognition on 2,000 pictures every second, which the company also claims is 20
times faster than it would take with a standard CPU.
More
specifically, it can perform 1.92 teraflops, or a trillion floating point
operations per second, when working with 16-bit floating point numbers. As
opposed to integers or whole numbers, floating point numbers—with decimal
points—are crucial to the calculations running through the neural networks
involved with deep learning.
Apple calls its
AI chip, part of the A11 Bionic chip, the Neural Engine. Again, it’s dedicated
to machine learning and deep learning tasks—recognizing your face, recognizing
your voice, recording animojis, and recognizing what you’re trying to frame in
the camera. It can handle some 600 billion operations per second, Apple claims.
App developers
can tap into this through Core ML, and easy plug-and-play way of incorporating
image recognition and other AI algorithms. Core ML doesn’t require the iPhone X
to run, but the Neural Engine handles these types of tasks faster. As with the
Huawei chip, the time spend offloading all this data processing to the cloud
should be vastly reduced, theoretically improving performance and again
lessening the strain on battery life.
And that’s
really what these chips are about: Handling the specific types of programming
tasks that machine learning, deep learning, and neural networks rely on, on the
phone, faster than the CPU or GPU can manage. When Face ID works in a snap,
you’ve likely got the Neural Engine to thank.
Is this the
future? Will all smartphone inevitably come with dedicated AI chips in future?
As the role of artificial intelligence on our handsets grows, the answer is
likely yes. Qualcomm chips can already use specific parts of the CPU for
specific AI tasks, and separate AI chips is the next step. Right now these
chips are only being utilized for a small subsection of tasks, but their
importance is going to only grow.
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