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1/ Apple's upcoming ARM MacBooks isn't just going to save them some money and run a bit faster. It marks the beginning of the end of the x86 era and Intel's four decade empire.

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2/ In the computer industry, victories are won through standards and scale. Intel invented the x86 standard. And by winning in the largest market of the 90s—PCs, it moved upmarket and eclipsed all server CPU vendors in a decade.
3/ Today smartphones are by far the largest computer market. At $600B, it’s larger than PCs and servers combined. Smartphones use ARM CPUs while PCs and servers use x86. Through the forces of standards and scale, it ought to displace x86 CPUs in PCs and servers. Why hasn’t it?
4/ In a word—developers. Developers are the third force that influences which platforms win or lose. Apps are written for phones but not on phones. Almost all code is written on x86 PCs or x86 servers in the cloud. x86 has developers locked in.
5/ Many companies have tried building ARM servers without success. Why? Linus Torvalds offers a simple explanation: developers want the cloud to run the same code as their laptops. Since devs code on x86 laptops, they want to deploy on x86 servers in the cloud.
6/ If Linus is right, the catalyst for the rest of the computer market to shift to ARM is a credible ARM based laptop for developers.

Enter Apple—and the rumors of its upcoming ARM powered MacBook. theverge.com/2020/3/27/2119…
7/ Apple isn’t just any PC vendor. It may only have 8% PC market share, but it has 29% share among sw developers. If Apple moves to ARM, it moves a large and highly influential developer base with it. With their laptops running ARM, devs will default to ARM in the cloud.
8/ Independent of Apple, ARM is going all-in on servers, promising 30% annual performance improvements. Using ARM’s Neoverse IP, Marvell, Ampere, Amazon, and others are building massive, Xeon-scale server CPUs.
9/ Amazon’s Graviton 2 ARM chip was just benchmarked by AnandTech—a website not known for hyperbole. Its conclusion? “An x86 Massacre.” ARM’s cost for the same performance was almost half of Intel and AMD x86 parts.
anandtech.com/show/15578/clo…
10/ Amazon switching to ARM should deeply worry Intel. AWS doesn’t just rent servers, it’s increasingly an OS, a dev platform. Intel’s Developer Forum last drew 6k attendees. AWS reinvent drew 60k. While CPUs were the target platforms of past eras, today’s target is the cloud.
11/ After decades of spectacular success by winning over developers, x86 now faces the threat of losing them. Apple & Amazon control 1/3 and 1/2 of local and cloud developers respectively. Both are re-platforming to ARM. When they’re done, so too will be the x86 era.
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James Wang
@jwangARK
ARK's Big Ideas 2020 deck is here—a year of research packed into 80 slides covering AI, robotics, autonomous, genomics, bitcoin, and more.

Download: ark-invest.com/big-ideas-2020

Here are 5 slides that really hit it home. Thread: Image
Deep learning continues to improve at astounding rates. In 2017 it costed $1,000 to train ResNet50 on the cloud, now it costs $10.

Thanks to AI accelerators and 'unlimited' compute in the cloud, AI algorithms are eating up 10x the amount of FLOPs per year. Image
They promised us flying cars and instead we got something even better—drones!

Drones are to flying cars as smartphones are to mainframes—they are cheaper, smaller, and serve more markets.

Everything from delivering Starbucks to moving people can be made 10x cheaper with drones. Image
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James Wang
@jwangARK
9/ Neural nets can consume GBs of memory. GPUs only have MBs of on-chip memory. So GPUs store neural nets on external memory soldered next to it on the PCB.

The problem is external memory is 10-100x slower & more power hungry vs. on-chip memory. They are also very expensive.
10/ Large models like Google’s Neural Machine Translation don’t even fit in one GPU’s external memory. Often they have to be split up across dozens of GPUs/servers. This increases latency by another 10-100x.

Ideally the whole model fits on a single chip—that's Cerebras' WSE.
11/ Cerebras’ Wafer Scale Engine (WSE) is *one chip* holding 400,000 cores and 18GB of memory. Neural network training happens on one piece of silicon rather than spread across dozens of boards, servers, interconnects. If it works, one chip can replace a rack of GPU servers.
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James Wang
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1/ Cerebras just built a chip with 50x the transistor count, 1,000x the memory and 10,000x the bandwidth of Nvidia’s flagship GPU. One such 'chip' could replace an entire rack of Nvidia GPUs.

What the heck is going on?
2/ It’s no coincidence that the fastest AI chip today—Nvidia’s V100—is also the largest @ 815mm^2. More area = more cores and memory.

But why not make the chip even bigger? Two reasons..
3/ First, the ASML lithography machines have a reticle size of 858mm^2. V100 is basically at the limit of what today's manufacturing allows.
Read 9 tweets

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