Page 47 - EE Times Europe Magazine | April2019

EE|Times EUROPE — Boards & Solutions Insert 47

Putting AI into the Edge Is a No-Brainer; Here’s Why

SOURCING AI CHIPS: IN-HOUSE OR THIRD PARTY?
Companies that manufacture smartphones and other devices vary in
their approaches to obtaining edge AI chips, with the decision driven
by factors such as phone model and, in some cases, geography. Some
buy application processor/modem chips from third-party providers,
such as Qualcomm and MediaTek, which together captured roughly
60% of the smartphone SoC market in 2018.
Both Qualcomm and MediaTek offer a range of SoCs at various
prices; while not all of them include an edge AI chip, the higher-
end offerings (including Qualcomm’s Snapdragon 845 and 855 and
MediaTek’s Helio P60) usually do.
At the other end of the scale, Apple does not use external AP chips
at all: It designs and uses its own SoC processors, such as the A11, A12,
and A13 Bionic chips, all of which have edge AI.
Other device makers, such as Samsung and Huawei, use a hybrid
strategy, buying some SoCs from merchant market silicon suppliers and
using their own chips (such as Samsung’s Exynos 9820 and Huawei’s
Kirin 970/980) for the rest.
OVER 50 AI ACCELERATOR COMPANIES VYING FOR EDGE AI
IN ENTERPRISE AND INDUSTRIAL
If edge AI processors used in smartphones and other devices are so
great, why not use them for enterprise applications, too? This has, in
fact, already happened for some use cases, such as for some autono-
mous drones. Equipped with a smartphone SoC application processor,
Figure 3: A die shot of the chip for Samsung’s Exynos 9820 shows a drone is capable of performing navigation and obstacle avoidance in
that about 5% of the total chip area is dedicated to AI processors. real time and completely on-device, with no network connection at all.
(Image: ChipRebel; Annotation: AnandTech) However, a chip optimized for a smartphone or tablet is not the right
choice for many enterprise or industrial applications. As discussed ear-
lier, the edge AI portion of a smartphone SoC accounts for only about
the components on a die-area basis, the Exynos’s edge AI neural 5% of the total area and about US$3.50 of the total cost and would
processing unit (NPU) represents roughly 5% of the chip’s total cost.
That translates to about US$3.50 each.
Similarly, Apple’s A12 Bionic chip dedicates about 7% of the die area
to machine learning. At an estimated US$72 for the whole processor,
that percentage suggests a cost of US$5.10 for the edge AI portion. The
Huawei Kirin 970 chip, estimated to cost the manufacturer US$52.50,
dedicates 2.1% of the die to the NPU, suggesting a cost of US$1.10.
(Die area is not the only way to measure what percentage of a chip’s
total cost goes toward AI, however. According to Huawei, the Kirin 970’s
NPU has 150 million transistors, representing 2.7% of the chip’s total
of 5.5 billion transistors. That would suggest a slightly higher NPU cost
of US$1.42).
Although the cited cost range is wide, it’s reasonable to assume that
NPUs cost an average of US$3.50 per chip. Multiplied by half a billion
smartphones (not to mention tablets, speakers, and wearables), that
makes for a large market, despite the low price per chip. At an average
cost of US$3.50 to the manufacturer, and a probable minimum of US$1,
adding a dedicated edge AI NPU to smartphone processing chips starts
looking like a no-brainer. Assuming normal markup, adding US$1 to
the manufacturing cost translates into only US$2 more for the end cus-
tomer. That means that NPUs and their attendant benefits — a better
camera, offline voice assistance, and so on — can be put into even a Figure 4: Apple’s A12 Bionic chip dedicates about 7% of the die
US$250 smartphone for less than a 1% price increase. area to machine learning. (Image: TechInsights/AnandTech)

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