我们采访了苹果芯片背后的男人——Johny Srouji!
Summary
TLDR在这段访谈中,苹果公司的Johnny Park讨论了iPhone 16和16 Pro系列的A18和A18 Pro芯片如何支持苹果智能技术。他强调了苹果智能技术对芯片设计的影响,以及8GB RAM在提升用户体验中的重要性。此外,他还提到了苹果在CPU核心数量、微架构设计和热管理方面的策略,以及苹果芯片在提升iPhone摄影能力方面的作用。
Takeaways
- 📱 iPhone 16和iPhone 16 Pro系列是首批搭载苹果智能技术的iPhone,A18和A18 Pro芯片在运行苹果智能技术方面发挥了重要作用。
- 🚀 A18和A18 Pro芯片在设计时就考虑了苹果智能技术,通过增强神经引擎、增加系统内存带宽和容量来提升整体性能。
- 💾 去年iPhone 15 Pro升级到8GB RAM,iPhone 16系列也继续这一配置,这不仅是为了支持苹果智能技术,也为了满足如高端游戏等其他应用的需求。
- 🎮 苹果智能技术是推动iPhone升级到8GB RAM的一个重要因素,但8GB RAM也将极大地促进其他应用的性能,包括高端游戏。
- 🤖 苹果的设计理念是构建统一的CPU架构,无论是手机、iPad还是Mac,以便于软件开发者能够针对单一架构进行开发。
- 🏋️♂️ 苹果的CPU设计注重单线程性能和能效,而不是简单地增加核心数量来追求峰值性能。
- 🔧 在决定CPU微架构时,苹果会根据产品需求、电池大小、电源系统和热环境等因素进行综合考虑。
- 🎮 苹果的GPU架构在iPhone和Mac之间保持一致,但会根据不同产品的特点进行实施上的差异化,以保持能效。
- 📹 iPhone 16系列在热管理方面进行了改进,以支持更强大的性能,同时保持了产品的轻薄设计。
- 🎥 苹果的图像信号处理器(ISP)和视频编码器的紧密集成,使得iPhone能够提供Dolby Vision 4K 120fps的视频拍摄能力。
Q & A
苹果A18和A18 Pro芯片如何支持运行苹果智能技术?
-A18和A18 Pro芯片通过持续改进神经引擎,增加系统内存带宽,并在整体性能上进行优化,从而支持苹果智能技术。这些芯片从设计之初就考虑了苹果智能技术的需求。
苹果智能技术对苹果硅芯片设计有何影响?
-苹果智能技术是苹果硅芯片设计中的一个重要考虑因素,特别是在决定内存容量和带宽时。苹果智能技术是推动iPhone 16 Pro系列升级到8GB RAM的关键因素之一。
为什么iPhone 16系列继续使用2个性能核心和4个效率核心的CPU配置?
-苹果的设计理念是提供最佳的用户体验,而不是追求特定的峰值性能。苹果的CPU在单线程性能和能效方面都是行业领先的,因此2个性能核心和4个效率核心的配置足以满足iPhone的需求。
苹果M4芯片相比A18系列在核心数量上有所增加,这是基于什么考虑?
-苹果M4芯片用于iPad等产品,这些设备有更大的散热空间和不同的电源供应,因此可以支持更高的频率和性能。苹果通过模拟和性能建模工具来决定不同产品的最佳核心配置。
苹果在CPU微架构设计上是如何做出决策的?
-苹果的决策基于提供最佳用户体验的原则,通过大量的数据和性能建模工具来确定最佳的CPU配置。苹果不会为了某个基准测试而设计产品,而是专注于整体的能效和性能。
苹果如何在iPhone上实现高端游戏的性能优化?
-苹果通过提供相似的GPU架构在不同设备上实现高性能,同时在实施层面进行调整,以适应不同的电源供应和散热限制。苹果的GPU设计注重能效,以确保在移动设备上也能提供持续的高性能。
苹果在iPhone 16系列中对散热管理做了哪些改进?
-苹果在iPhone 16系列中进行了多项散热改进,以提供更好的产品体验。这些改进包括使用新材料和优化组件布局,同时保持设备的轻薄设计。
苹果硅芯片在iPhone的视频拍摄能力中扮演了什么角色?
-苹果硅芯片中的图像信号处理器(ISP)和视频编码器在视频拍摄中发挥了关键作用。ISP负责处理图像的色彩和色调,而视频编码器则以高速度压缩视频,使得iPhone能够支持杜比视界4K 120帧每秒的视频录制。
苹果如何在不同设备上保持GPU架构的一致性,同时又满足各自的特点?
-苹果通过在不同设备上使用相似的GPU架构,同时在实施层面进行调整,如核心数量和频率,来保持一致性。这样的设计使得开发者可以更容易地将游戏和应用移植到不同的苹果设备上。
苹果如何决定在iPhone中使用特定的散热解决方案?
-苹果在决定散热解决方案时,会考虑多种因素,包括材料、设备厚度、电池空间和其他组件的布局。苹果的目标是提供最佳的产品体验,而不是单纯追求某个方面的性能。
Outlines
📱 iPhone 16系列的A18芯片和Apple智能
在这段对话中,讨论了iPhone 16和iPhone 16 Pro系列的A18和A18 Pro芯片如何支持Apple智能技术。提到了Apple在2017年首次引入神经引擎,并逐年改进以提高性能和能效。A18和A18 Pro芯片进一步增强了神经引擎,增加了系统内存带宽,并改善了整体计算、内存子系统。此外,提到了iPhone 15 Pro去年升级到8GB RAM,而iPhone 16系列也采用了8GB RAM,这不仅是为了Apple智能,也为了支持高端游戏等其他应用。
🔋 Apple芯片设计的核心原则与性能
这部分内容讨论了Apple芯片设计的核心原则,包括跨不同设备的CPU架构一致性、不追求特定峰值性能的基准测试,以及追求最高能效的性能。强调了Apple芯片在单线程性能和能效方面领先行业,并解释了为何在iPhone上使用2个性能核心和4个效率核心的配置。同时,提到了Apple M4芯片在iPad上增加核心数的原因,以及如何在不同的设备上实现相同架构的不同实现,以适应不同的热环境和电源需求。
🎮 将AAA游戏移植到iPhone的挑战与解决方案
在这段对话中,讨论了将AAA游戏移植到iPhone时面临的工程挑战,包括移动GPU与桌面或游戏机GPU之间的固有差异,如内存子系统带宽较低、渲染管线不同等。Apple通过保持GPU架构的一致性来简化开发过程,同时在不同设备上进行特定的实现调整。此外,还讨论了iPhone 16系列在热管理方面的改进,包括与产品团队合作,优化整体产品以提供最佳用户体验,而不是单纯追求峰值性能。
🎥 iPhone在视频拍摄方面的创新与Apple硅片的作用
这部分内容强调了iPhone在视频拍摄能力方面的优势,特别是A18系列芯片在iPhone 16和16 Pro中的作用。Apple在图像信号处理器(ISP)和媒体引擎方面的投资,使得每帧图像都能进行高质量的色彩和色调分析。通过与神经引擎的紧密集成,iPhone能够快速处理图像并提供详细的场景信息。此外,视频编码器能够以高速度压缩视频,使得iPhone能够支持Dolby Vision 4K 120fps的视频录制,这得益于Apple硅片、ISP和软件之间的紧密集成。
Mindmap
Keywords
💡Apple 硅片
💡Apple 智能
💡神经引擎
💡Transformer 模型
💡内存带宽
💡能效
💡单线程性能
💡图形处理器(GPU)
💡热管理
💡图像信号处理器(ISP)
Highlights
苹果在2017年首次引入神经引擎,此后每年都在提升性能和能效。
iPhone 16和16 Pro系列的A18和A18 Pro芯片在设计时就考虑了苹果智能,提升了神经引擎性能。
A18和A18 Pro芯片增加了系统内存带宽,以支持苹果智能和其他高性能需求。
iPhone 16系列继续采用8GB RAM,这一决策不仅由苹果智能驱动,也考虑了游戏等其他应用场景。
苹果的软件团队会优化内存使用,避免浪费,确保最佳用户体验。
苹果M4芯片增加了核心数量,而A18系列则保持了两个性能核心和四个效率核心的配置。
苹果的CPU架构设计旨在提供最佳性能和能效,而不是单纯追求峰值性能。
苹果的产品设计哲学是为特定产品需求定制优化,而不是追求最大核心数量。
苹果的微架构决策基于性能模型和实际数据,以确保最佳用户体验。
苹果的GPU架构在iPhone和Mac之间保持一致,便于开发者进行游戏移植。
iPhone 16系列的热管理系统进行了改进,以提供更好的性能和能效。
苹果在iPhone 16系列中使用了铝板进行热管理,以优化产品的整体设计。
苹果的图像信号处理器(ISP)和媒体引擎紧密集成,提供卓越的视频拍摄能力。
iPhone 16系列能够以每秒120帧的速度拍摄Dolby Vision 4K视频,得益于苹果芯片的强大性能。
Transcripts
Park
Johnny hi Johnny I'm R from J and nice
to have you here today my pleasure good
afternoon my pleasure to meet you so I
heard you are kind of straightforward
guide on Tech topics so maybe we can
just step into the questions ASAP go
ahead please so my first questions being
obviously iPhone 16 and the iPhone 16
Pro Series are probably the first
iPhones after you launch the Apple
intelligence so how do a18 and a18 pro
chips contribute to running Apple
intelligence and for wider pers
perspective has Apple intelligence make
any significant impact on your decision
making when you're designing Apple
silons and maybe overall Hardware yeah U
so you're referring to Apple
intelligence running on app silicon for
the iPhone but we actually introduce and
shipped our first neural engine
implementation in 2017 and since then
we've been improving our neural engine
year-over-year and adding more
performance with power efficiency and
our colleagues from the software team
has been leveraging and utilizing that
engine then when you look at Apple
intelligence running an apple silicon it
utilizes the whole s so but it heavily
also utilizes the neural engine which we
also added support for Transformer
models many years back and that's one of
the reasons that7 Pro can support apple
intelligence so with A8 and 18 Pro we
took it even one step further we kept
improving the neural engine even more
than a17 Pro we added more bandwidth to
the system memory and we added more
improvements across the whole so to to
build a balanced compute memory
subsystem including the capacity and the
bandwidth on the computer so we built it
from the ground up with apple
intelligence in mind you actually
upgraded to 8 gig of RAM on iPhone 15
Pro last year if I'm not mistaken the
iPhone 16 series are also you know
having the upgrade for 8 gigs of RAM
which for me seems like a you know
critical turning point so could you
explain the necessity of you know
bringing more Rams to iPhone is this
decision solely driven by Apple
intelligence or were other use cases
such as gaming also considered so again
our goal is to build the best products
delivering the absolute best user
experience as it relates to Apple
intelligence dram is one aspect and when
we look at what we're building whether
it's silicon Hardware or software we
don't want to be wasteful in many ways
we have lots of data that tells us what
is going to enable a certain feature and
apple intelligence is one of those very
very important features that we want to
enable and we look at different
configurations both for computation and
memory bandwidth and memory capacity and
then we made the right tradeoff and
balance of what actually makes the most
sense so Apple intelligence was a major
feature that led us to believe that we
we need to get to 8 gab but having said
that the 8 gab is going to help
immensely in many other applications
including gaming a high highend gaming
AAA title games and highend gaming on
device so I think it's going to be
really really beneficial the other thing
to keep in mind uh this is one of the
benefits of having the software and the
silicon and the product fully integrated
is that the software team our excellent
software team will optimize not only for
compute they'll also optimize for the
memory footprint of each application so
they don't end up also wasting memory so
we look at all these trade-offs and we
end up with here's what makes sense and
8 GB was the most perfect choice for us
I think would be a par question you know
some of our competitors are having
started to put more core counts into
their CPU designs even the latest Apple
M4 chip has also increased the number of
core counts compared to before and in
contr the a18 series continue to use
let's say two performance cores and four
efficiency cores it's a strategy that
you have been used for quite some times
why did the apple M4 cons considered
increasing the numbers of core counts
while a18 Pro didn't great question so
maybe I will start with our philosophy
and then I'll get to your to answer
specifically your question our
philosophy I I'll give you some uh some
principles one of the principles is we
want to build the same CPU architecture
whether it's for a chip that goes into
the phone or the iPad or the Mac or
other configurations so it's a scalable
architecture same applies to the
graphics neural engine and others now
one of the main benefits for that is for
the software and developers you have a
single architecture that you develop for
the iPhone or the iPad or the Mac so
that's big and by the way another side
benefit is from a team efficiency you
get to design one architecture so that's
one principle some architecture across
different chips the second principle is
given we're not a merchant vendor we
don't really need to Target a specific
Peak Performance for a specific Corner
case Benchmark that you may not even
experience or hit as a customer just in
order to win some Benchmark we care
about again delivering the absolute best
user experience and for that we look at
lots of lots of data of how the devices
the software is using the silicon and
what makes the absolute best use and
therefore we make that R based on that
aspect a third principle which is
important is you want to deliver the
absolute best performance whether it's a
CPU or Graphics but let's talk about CPU
since that's your question with the
highest performance at the best Absolute
Energy Efficiency Energy Efficiency
extremely extremely important for us so
the best single thread and that's key
because what others might be doing other
vendors what they might be doing is they
add multiple cores more and more cores
in order to compensate for not so good
single threat performance so one way to
compensate is you add more cores uh and
therefore you can achieve a certain Peak
Performance at higher power larger die
which means also larger cost so we don't
do that either so now let me answer your
question based on these principles when
you look at the single thread of
performance core across all of our
silicon is the absolute best in the
industry we're leading the industry if
you look at the efficiency cores same
we're at the absolute best we're like
leading in a big way and then when we
look at the configuration whether it's a
selic that goes into the phone or the
iPad or the mac and we have lots of
simulation uh and performance modeling
tools and we look at actual data
and then we take into account for
example the battery size for a product
the power delivery system for a product
the thermal envelope for the product
because overbuilding again is wasteful
for example for the phone we came to the
conclusion that 2 p4e so two performance
course for efficiency course meets the
needs of what that device requires
because we have the absolute best single
thread and the efficiency cause is so
good for other tasks and that
configuration works then your next
question was why did you make different
choice for M4 that goes into the iPad
those have larger thermal envelopes
different Power delivery and therefore
you can push the frequency and the
performance to a different point so
going to my first principle of using the
same architecture across different chips
we make the differentiation in
implementation of frequency points
operating points we enable Peak
frequency in order to enable what we
call best performance to give you the
absolute best performance but we also
look into Energy Efficiency and where
the sustained needs to be and what is
the shape of the curve when you look at
Power performance so that your high
performance maximum Energy
Efficiency okay so the next question is
about the microarchitecture you actually
have a long period of time that micro
architecture of a series CPUs was
steadily updated before A6 I would say
starting with A7 Pro you actually
started to widen the your CPU of course
uh we saw a 9 wide decode on a17 Pro and
depends on the CPU upgrade we saw on
Apple M4 I would say we expect a18
series CPU to widen further so my
question is when you are making those
decisions on CPU micro architectures you
know how PPA trade-offs are made So my
answer actually follows the same
principle that just cover which is you
want to design the best CPU for a
certain silicon that goes into a certain
product and we have lots of data and
performance modeling to tell us what
configuration makes the best use terms
of highest power efficiency and
performance obviously we're not going to
get into micro architecture details on
what plans we have in mind but you can
imagine we have a deep line of CPU micro
architecture not for this year next year
for many years to come and we have
modeling tools including what cash sizes
you need for each of these course per
implementation based on that we make
those decisions and based on what's
going to deliver the best user
experience again not for a certain
Benchmark now in my experience it proved
to be the case that once you do that you
actually end up winning lots of
benchmarks and winning those while
keeping your Energy Efficiency so it
becomes a side benefit but again the
strength that we have being an
integrated part of Apple the full
integration of the software the product
team and the Silicon team where you
design absolutely for what our product
needs not for everyone else gives us the
freedom to optimize what you call PPA
power performance area for the absolute
best Energy Efficiency and area it's
science but it's science combined with
art and you make the proper judgment
codes based on tools and modeling of
what actually going to deliver the best
user experience so it's both okay so
next question is about uh gaming on
iPhones we saw developers bringing more
and more AAA games to iPhone but
obviously there are many inherent
difference between mobile gpus and
desktop or console gpus I say typically
mobile memory subst system would be you
know have much lower bandwidth mobile
GPU rendering pipeline actually differs
for example Apple's GPU are actually
utilizing tile based defer rendering
techniques instead of immediate
rendering and also software development
pipelines are quite you know distinct so
I'm wondering what engineering
challenges did you guys face when
deciding to Port these triaa games to
iPhone and how were they addressed and
by the way again following one of the
principle I just covered which is we
want to build the same GPU architecture
for all of our chips for example you can
see that the GPU architecture
fundamental that we built for the mac
and for the iPhone are very similar the
implementation are different and we can
get to that later but the architecture
is is similar and what that gives again
to developers is they get to port or
Implement a game for the iPhone and the
mac and it's the same porting so that's
great and the fact that our GPU is so
performing that means that for example
when you look at the phone including 18
Pro is that it has longevity meaning you
can looking forward for future games it
will be able to support those at really
high performance low power then when you
look at different implementations
whether it's a phone or a Mac this is
where we make implementation differences
and we're smart about how we do that
during the implementation while keeping
the architecture similar and it's not
only frequency and operating points it's
below that even meaning a lower level of
details but in terms of the functional
archit ecture is is the same of course
you're working with different Power
delivery and thermal envelopes which is
why you can see that appid silicon
shipping on a phone has different or
less GPU cores than appid silicon
shipping in a MacBook Pro but the
fundamental what we call fstp the
shaders Etc are very very similar and
then we operate at different points
again to maintain Energy Efficiency but
the same grade Graphics you get all
across we just talk about the running
games on iPhone and also seral
management is uh crucial factor for that
this year you emphasized improvements on
thermal management so can you
specifically talk about what changes
you've made for the Thal management the
iPhone 16 and the 16 Pro Series and
could you talk through decision making
and also how does it differs from
previous thermos systems again we build
products and the idea is to deliver the
best products and it's not only about
theque performance for graphics at
certain termal envelope and again this
is where my team and the product team
work hand in hand ahead of time about
what is the absolute best decision we
can make at the product level of course
in terms of silicon you want more
thermos you want bigger battery but that
has to off on other aspects so we need
to manage that piece and we highlight in
the event this morning about some of the
many thermal improvements that were were
made into the iPhone 16 series and that
obviously benefits theid silicon that is
enabling those iPhones but then you go
back to the Energy Efficiency the fact
that we have the most energy efficient
chips it helps us and again when you
don't chase Peak Performance at higher
power and not so energy efficient versus
sustained and best in performance you
get to optimize for the overall system
obviously we can get bit deeper into
that you guys already implemented if I'm
not wrong aluminum plate inside phone
actually in the industry a lot of people
are using let's say Vapor Chambers and
other Solutions so when you are deciding
new thermal solution what kind of as a
mindset did you go through what kind of
factors do you considers you know again
the mindset is who want to deliver the
best product not necessarily one thing
the best product optimized for the
absolute best user experience for
example you can deliver a better thermal
envelope if you have thicker devices you
use different
materials the example you gave is one
more example but that's one tradeoff we
believe if you take it to the extreme
doesn't actually benefit the product
because it has implications on the form
factor and the ID so we take all
consideration to account including the
materials and how thick you can allow
that product we want it thinner how much
space you give battery and other
components even the placement of
components on the MLB make a difference
so it's very thoughtful thorough process
that takes into consideration many many
aspects but the absolute number one
priority is building for the absolute
products not just thermal so that I can
deliver a Peak Performance and saying I
win this Benchmark we want to deliver
the best absolute product and and the
Energy Efficiency Focus we have all
across enables all of this and again
it's an optimization at the product
level okay so the last question let's
talk about video shooting on iPhone now
so uh obviously iPhone have very great
humaning capability compared to
competitors what kind of roles did Apple
silicons made in that kind of process
well maybe for example A8 series inside
iPhone 16 and 16 pros of course this
started many many many years back uh
we've been investing in our image
silicon processor which is the ISP and
same applies for other media engines so
we've been investing in that space we
believe we have the best absolute custom
silicon that is built for these devices
for example if you look at each frame
that passes through our camera ISP it
gets analyzed for things like color and
tone when you look at the image signal
processing as a pipeline it starts with
a an excellent image information that
gets into the high speed and then it
gets defined even further by dedicated
streaming machine learning based
processing that have been trained on
millions of photos before that and those
run on the new engine so you can see the
tight integration between the new engine
and the IP in this case now the new
engine also provides detailed semantic
information about the overall scene
including the subjects various subjects
and the intent of the photographers now
what this does it enables the camera to
deliver a wider range of dynamic
information based on the scene and the
materials in the scene Etc and we get to
do that very very fast at a high rate
124k frames per second so that's great
then post ISP as as that gets processed
we have another media engine called the
video encoder that compresses the video
at 1 billion pixels per second so when
you combine both you get to a very high
rate that's how we enable Dolby Vision
4K 120 frames per second sometimes you
can see that others you know our
competitors can generate through lots of
software processing a good frame a good
picture at one time but we're first in
terms of delivering a video for D Vision
Video 4K 120 capture um and that's
thanks to the tight integration of the
Silicon different IPS on the software
and the camera control it's like the
physics camera control and the Silicon
including the ISP they all work together
I think that's all the question we have
today so thank you my pleasure thank you
thank you thank
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