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As it closes in on Arm, Nvidia announces UK supercomputer dedicated to medical research
As Nvidia continues to work through its deal to acquire Arm from SoftBank for $40 billion, the computing giant is making another big move to lay out its commitment to investing in U.K. technology. Today the company announced plans to develop Cambridge-1, a new £40 million AI supercomputer that will be used for research in the health industry in the country, the first supercomputer built by Nvidia specifically for external research access, it said.
Nvidia said it is already working with GSK, AstraZeneca, London hospitals Guy’s and St Thomas’ NHS Foundation Trust, King’s College London and Oxford Nanopore to use the Cambridge-1. The supercomputer is due to come online by the end of the year and will be the company’s second supercomputer in the country. The first is already in development at the company’s AI Center of Excellence in Cambridge, and the plan is to add more supercomputers over time.
The growing role of AI has underscored an interesting crossroads in medical research. On one hand, leading researchers all acknowledge the role it will be playing in their work. On the other, none of them (nor their institutions) have the resources to meet that demand on their own. That’s driving them all to get involved much more deeply with big tech companies like Google, Microsoft and, in this case, Nvidia, to carry out work.
Alongside the supercomputer news, Nvidia is making a second announcement in the area of healthcare in the U.K.: it has inked a partnership with GSK, which has established an AI hub in London, to build AI-based computational processes that will be used in drug vaccine and discovery — an especially timely piece of news, given that we are in a global health pandemic and all drug makers and researchers are on the hunt to understand more about, and build vaccines for, COVID-19.
The news is coinciding with Nvidia’s industry event, the GPU Technology Conference.
“Tackling the world’s most pressing challenges in healthcare requires massively powerful computing resources to harness the capabilities of AI,” said Jensen Huang, founder and CEO of Nvidia, in his keynote at the event. “The Cambridge-1 supercomputer will serve as a hub of innovation for the U.K., and further the groundbreaking work being done by the nation’s researchers in critical healthcare and drug discovery.”
The company plans to dedicate Cambridge-1 resources in four areas, it said: industry research, in particular joint research on projects that exceed the resources of any single institution; university granted compute time; health-focused AI startups; and education for future AI practitioners. It’s already building specific applications in areas, like the drug discovery work it’s doing with GSK, that will be run on the machine.
The Cambridge-1 will be built on Nvidia’s DGX SuperPOD system, which can process 400 petaflops of AI performance and 8 petaflops of Linpack performance. Nvidia said this will rank it as the 29th fastest supercomputer in the world.
“Number 29” doesn’t sound very groundbreaking, but there are other reasons why the announcement is significant.
For starters, it underscores how the supercomputing market — while still not a mass-market enterprise — is increasingly developing more focus around specific areas of research and industries. In this case, it underscores how health research has become more complex, and how applications of artificial intelligence have both spurred that complexity but, in the case of building stronger computing power, also provides a better route — some might say one of the only viable routes in the most complex of cases — to medical breakthroughs and discoveries.
It’s also notable that the effort is being forged in the U.K. Nvidia’s deal to buy Arm has seen some resistance in the market — with one group leading a campaign to stop the sale and take Arm independent — but this latest announcement underscores that the company is already involved pretty deeply in the U.K. market, bolstering Nvidia’s case to double down even further. (Yes, chip reference designs and building supercomputers are different enterprises, but the argument for Nvidia is one of commitment and presence.)
“AI and machine learning are like a new microscope that will help scientists to see things that they couldn’t see otherwise,” said Dr. Hal Barron, chief scientific officer and president, R&D, GSK, in a statement. “NVIDIA’s investment in computing, combined with the power of deep learning, will enable solutions to some of the life sciences industry’s greatest challenges and help us continue to deliver transformational medicines and vaccines to patients. Together with GSK’s new AI lab in London, I am delighted that these advanced technologies will now be available to help the U.K.’s outstanding scientists.”
“The use of big data, supercomputing and artificial intelligence have the potential to transform research and development; from target identification through clinical research and all the way to the launch of new medicines,” added James Weatherall, PhD, head of Data Science and AI, AstraZeneca, in his statement.
“Recent advances in AI have seen increasingly powerful models being used for complex tasks such as image recognition and natural language understanding,” said Sebastien Ourselin, head, School of Biomedical Engineering & Imaging Sciences at King’s College London. “These models have achieved previously unimaginable performance by using an unprecedented scale of computational power, amassing millions of GPU hours per model. Through this partnership, for the first time, such a scale of computational power will be available to healthcare research – it will be truly transformational for patient health and treatment pathways.”
Dr. Ian Abbs, chief executive & chief medical director of Guy’s and St Thomas’ NHS Foundation Trust Officer, said: “If AI is to be deployed at scale for patient care, then accuracy, robustness and safety are of paramount importance. We need to ensure AI researchers have access to the largest and most comprehensive datasets that the NHS has to offer, our clinical expertise, and the required computational infrastructure to make sense of the data. This approach is not only necessary, but also the only ethical way to deliver AI in healthcare – more advanced AI means better care for our patients.”
“Compact AI has enabled real-time sequencing in the palm of your hand, and AI supercomputers are enabling new scientific discoveries in large-scale genomic data sets,” added Gordon Sanghera, CEO, Oxford Nanopore Technologies. “These complementary innovations in data analysis support a wealth of impactful science in the U.K., and critically, support our goal of bringing genomic analysis to anyone, anywhere.”
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Arm’s financials and the blurring future of the semiconductor sector
Amidst the blitz of SoftBank earnings news today comes the financials for all of SoftBank’s subsidiaries, which includes Arm Holdings, the most important chip design and research company in the world that SoftBank bought for $32 billion back in 2016. Arm produces almost all of the key designs for the chips that run today’s smartphones, including Apple’s A13 Bionic chip that powers its flagship iPhone. In all, 22.8 billion chips were shipped globally last year using Arm licenses according to SoftBank’s financials.
It’s a massively important company, and its finances show a complicated picture for itself — and the semiconductor industry at large.
We sat down with Arm Holding’s CEO Simon Segars last year to discuss the company’s growing appetite for ambitious research, fueled by SoftBank dollars and the bullish vision of the conglomerate’s chairman Masayoshi Son:
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Cartesiam helps developers bring AI to microcontrollers
Cartesiam, a startup that aims to bring machine learning to edge devices powered by microcontrollers, has launched a new tool for developers who want an easier way to build services for these devices. The new NanoEdge AI Studio is the first IDE specifically designed for enabling machine learning and inferencing on Arm Cortex-M microcontrollers, which power billions of devices already.
As Cartesiam GM Marc Dupaquier, who co-founded the company in 2016, told me, the company works very closely with Arm, given that both have a vested interest in having developers create new features for these devices. He noted that while the first wave of IoT was all about sending data to the cloud, that has now shifted and most companies now want to limit the amount of data they send out and do a lot more on the device itself. And that’s pretty much one of the founding theses of Cartesiam. “It’s just absurd to send all this data — which, by the way, also exposes the device from a security standpoint,” he said. “What if we could do it much closer to the device itself?”
The company first bet on Intel’s short-lived Curie SoC platform. That obviously didn’t work out all that well, given that Intel axed support for Curie in 2017. Since then, Cartesiam has focused on the Cortex-M platform, which worked out for the better, given how ubiquitous it has become. Since we’re talking about low-powered microcontrollers, though, it’s worth noting that we’re not talking about face recognition or natural language understanding here. Instead, using machine learning on these devices is more about making objects a little bit smarter and, especially in an industrial use case, detecting abnormalities or figuring out when it’s time to do preventive maintenance.
Today, Cartesiam already works with many large corporations that build Cortex-M-based devices. The NanoEdge Studio makes this development work far easier, though. “Developing a smart object must be simple, rapid and affordable — and today, it is not, so we are trying to change it,” said Dupaquier. But the company isn’t trying to pitch its product to data scientists, he stressed. “Our target is not the data scientists. We are actually not smart enough for that. But we are unbelievably smart for the embedded designer. We will resolve 99% of their problems.” He argues that Cartesiam reduced time to market by a factor of 20 to 50, “because you can get your solution running in days, not in multiple years.”
One nifty feature of the NanoEdge Studio is that it automatically tries to find the best algorithm for a given combination of sensors and use cases and the libraries it generates are extremely small and use somewhere between 4K to 16K of RAM.
NanoEdge Studio for both Windows and Linux is now generally available. Pricing starts at €690/month for a single user or €2,490/month for teams.
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Arm brings custom instructions to its embedded CPUs
At its annual TechCon event in San Jose, Arm today announced Custom Instructions, a new feature of its Armv8-M architecture for embedded CPUs that, as the name implies, enables its customers to write their own custom instructions to accelerate their specific use cases for embedded and IoT applications.
“We already have ways to add acceleration, but not as deep and down to the heart of the CPU. What we’re giving [our customers] here is the flexibility to program your own instructions, to define your own instructions — and have them executed by the CPU,” ARM senior director for its automotive and IoT business, Thomas Ensergueix, told me ahead of today’s announcement.
He noted that Arm always had a continuum of options for acceleration, starting with its memory-mapped architecture for connecting over a bus GPUs and today’s neural processor units. This allows the CPU and the accelerator to run in parallel, but with the bus being the bottleneck. Customers also can opt for a co-processor that’s directly connected to the CPU, but today’s news essentially allows Arm customers to create their own accelerated algorithms that then run directly on the CPU. That means the latency is low, but it’s not running in parallel, as with the memory-mapped solution.
As Arm argues, this setup allows for the lowest-cost (and risk) path for integrating customer workload acceleration, as there are no disruptions to the existing CPU features and it still allows its customers to use the existing standard tools with which they are already familiar.
For now, custom instructions will only be available to be implemented in the Arm Cortex-M33 CPUs, starting in the first half of 2020. By default, it’ll also be available for all future Cortex-M processors. There are no additional costs or new licenses to buy for Arm’s customers.
Ensergueix noted that as we’re moving to a world with more and more connected devices, more of Arm’s customers will want to optimize their processors for their often very specific use cases — and often they’ll want to do so because by creating custom instructions, they can get a bit more battery life out of these devices, for example.
Arm has already lined up a number of partners to support Custom Instructions, including IAR Systems, NXP, Silicon Labs and STMicroelectronics .
“Arm’s new Custom Instructions capabilities allow silicon suppliers like NXP to offer their customers a new degree of application-specific instruction optimizations to improve performance, power dissipation and static code size for new and emerging embedded applications,” writes NXP’s Geoff Lees, SVP and GM of Microcontrollers. “Additionally, all these improvements are enabled within the extensive Cortex-M ecosystem, so customers’ existing software investments are maximized.”
In related embedded news, Arm also today announced that it is setting up a governance model for Mbed OS, its open-source operating system for embedded devices that run an Arm Cortex-M chip. Mbed OS has always been open source, but the Mbed OS Partner Governance model will allow Arm’s Mbed silicon partners to have more of a say in how the OS is developed through tools like a monthly Product Working Group meeting. Partners like Analog Devices, Cypress, Nuvoton, NXP, Renesas, Realtek,
Samsung and u-blox are already participating in this group.
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ARM shows SoftBank does tech PE the right way
Private equity firms get a bad rap — and not without reason. In the prototypical example, a bunch of men in suits (and these folks always seem to be men for some reason) swoop in from Manhattan with Excel spreadsheets and pink slips, slashing and burning through an organization while ladening the balance sheet with debt in an algebraic alchemy of monetary extraction.
Vultures, parasites, octopuses — these are folks who almost certainly won popularity contests in high school and now seem to be shooting for most unpopular person to be compared to a crustacean in the Finance section of the WSJ (and there is some damn strong competition in those pages).
Sometimes that restructuring can save an org, and yes, many companies need a Marie Kondo armed with a business plan. But it’s a model that works best for, say, retail chains, and traditionally has been wholly incompatible with the tech industry.
Tech is a tough place for private equity buyouts, as the biggest expense for most companies is talent (i.e. R&D), and cutting R&D is usually the quickest path to cutting the valuation of the asset you just acquired. Unlike retail or manufacturing, there are just fewer cost levers to manipulate to make the numbers look better, and so PE firms have generally shied away from big tech acquisitions.
So it was interesting talking to Simon Segars this week in New York. Segars is the CEO and longtime executive at ARM Holdings, the U.K.-headquartered chip designer that powers billions of devices worldwide. Over the past two decades, ARM has had an incredible run: Last year, its designs were imprinted on 22.9 billion chips, thanks largely to the now ubiquitous adoption of smartphones across the world.
That success has been under stress though. As Brian Heater analyzed in his State of the Smartphone, smartphone growth has slowed in most markets as consumers extend their upgrade cycles and the pace of innovation has slowed. Add in the ongoing trade kerfuffle between the U.S. and China, and suddenly being the worldwide leading designer of smartphone chips isn’t as enviable as it was even just a few years ago.
As a public company facing this landscape, ARM would have faced incredible pressure from investors to meet short-term revenue targets while cutting back on R&D — the very source of future growth the company has relied on its entire history. But ARM isn’t a public company — instead, SoftBank founder and CEO Masayoshi Son bought out the company entirely in 2016 for $32 billion.
Rather than being pegged to its stock price or a quick return to a PE shop, ARM is now seemingly evaluated on growth in its intellectual property and strategy for capturing new markets. “I’m in a very fortunate position where, despite the slowing of the smartphone market … I’ve got an owner that says, invest, you know, invest like crazy to make sure you capture these ways of growth in the future, which is what we’re doing,” Segars explained to TechCrunch.
The company could have just doubled down on its existing product lines, but SoftBank’s ownership has opened the floodgates to explore other areas that could use ARM expertise. The company is now focused (if one can focus on many things) on everything from 5G and networking to IoT and autonomous driving. “We look to be in the right place at the right time with the right technology to catch the upswing into the future,“ Segars said.
That strategy requires some serious audacity though. ARM’s EBITDA was $225 million last year (21% lower than the year before) on $1.8 billion in net sales, which year-over-year grew a paltry 0.2% according to SoftBank’s latest financials. Meanwhile, operating expenses are up from the addition of hundreds of new employees and a new headquarters campus in Cambridge, outside London. R&D isn’t cheap, nor does it payoff quickly.
Yet, that is exactly how Son and SoftBank approach this take-private transaction. “During the acquisition process, Masa said to me, ‘You run the business, I only care about long-term strategy, not going to interfere, you know, you know what you’re doing.’ … [and] Masa has been absolutely true to his word on that,” Segars said. “From a day-to-day basis, SoftBank leaves us completely alone.”
And unlike the bean counters that plague most PE shops, Son isn’t interested in detailed operational data from the firm. “When I give tactical updates… he’s asleep, [but] try stopping him when he’s talking about long-term strategy,” Segars said.
And unlike the PE model of dumping a bunch of high-interest corporate debt on the balance sheet to eke out returns, SoftBank has — at least, so far — avoided that particular tactic. While there were ruminations that SoftBank was considering cashing out some dollars from ARM using loans early last year, such rumors have apparently not panned out. Segars confirmed that “we have none” when we asked about leverage, which has otherwise plagued much of the rest of SoftBank Group and its various entities.
While ARM clearly has a bullish owner who somehow uses financial wizardry to give the company the resources it needs to grow, Son doesn’t have an infinite timeline for the company. Much like classic PE firms with five to seven-year time horizons to harvest returns, Son has already spoken out loud about pushing ARM back into the public markets in roughly five years’ time.
“I’m pretty sure, the night before we go public again, I’m going to be thinking ‘Man, I wish we’d had more time, you know, five years sounds like a lot,” Segars said. But “the way I talk about it within ARM is we’re in an investment phase now … and the goal is that by the time we re-list … the revenues from these new markets are taking off and that’s flowing to the bottom line and we get back to a world of growing top line and expanding margins.”
In other words, ARM is a classic PE deal, but with the focus on actually getting the fundamentals in the business right without that financial alchemy and employee firing sadness. Maybe the plan will work, or maybe it won’t, but it is the right approach to handling the growth of a tech company.
How many other tech companies could use such an approach? How many other companies are currently languishing if only they had more focused owners with a true growth mindset to invest in the future? Silicon Valley has created trillions of dollars in market value over the past two decades, but there is even more waiting to be unlocked. And the best part is, it doesn’t even require an Excel macro to make it happen.
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At this point, SoftBank Group is really just its Vision Fund
Last week, SoftBank Group Corp. — Masayoshi Son’s holding company for his rapidly expanding collection of businesses — reported its fiscal year financials. There were some major headlines that came out of the news, including that the company’s Vision Fund appears to be doing quite well and that SoftBank intends to increase its stake in Yahoo Japan.
Now that the dust has settled a bit, I wanted to dive into all 80 pages of the full financial results to see what else we can learn about the conglomerate’s strategy and future.
The Vision Fund is just dominating the financials
We talk incessantly about the Vision Fund here at TechCrunch, mostly because the fund seems to be investing in every startup that generates revenue and walks up and down Sand Hill looking for capital. During the last fiscal year ending March 31st, the fund added 36 new investments and reached 69 active holdings. The total invested capital was a staggering $60.1 billion.
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Arm expands its push into the cloud and edge with the Neoverse N1 and E1
For the longest time, Arm was basically synonymous with chip designs for smartphones and very low-end devices. But more recently, the company launched solutions for laptops, cars, high-powered IoT devices and even servers. Today, ahead of MWC 2019, the company is officially launching two new products for cloud and edge applications, the Neoverse N1 and E1. Arm unveiled the Neoverse brand a few months ago, but it’s only now that it is taking concrete form with the launch of these new products.
“We’ve always been anticipating that this market is going to shift as we move more towards this world of lots of really smart devices out at the endpoint — moving beyond even just what smartphones are capable of doing,” Drew Henry, Arms’ SVP and GM for Infrastructure, told me in an interview ahead of today’s announcement. “And when you start anticipating that, you realize that those devices out of those endpoints are going to start creating an awful lot of data and need an awful lot of compute to support that.”
To address these two problems, Arm decided to launch two products: one that focuses on compute speed and one that is all about throughput, especially in the context of 5G.
The Neoverse N1 platform is meant for infrastructure-class solutions that focus on raw compute speed. The chips should perform significantly better than previous Arm CPU generations meant for the data center and the company says that it saw speedups of 2.5x for Nginx and MemcacheD, for example. Chip manufacturers can optimize the 7nm platform for their needs, with core counts that can reach up to 128 cores (or as few as 4).
“This technology platform is designed for a lot of compute power that you could either put in the data center or stick out at the edge,” said Henry. “It’s very configurable for our customers so they can design how big or small they want those devices to be.”
The E1 is also a 7nm platform, but with a stronger focus on edge computing use cases where you also need some compute power to maybe filter out data as it is generated, but where the focus is on moving that data quickly and efficiently. “The E1 is very highly efficient in terms of its ability to be able to move data through it while doing the right amount of compute as you move that data through,” explained Henry, who also stressed that the company made the decision to launch these two different platforms based on customer feedback.
There’s no point in launching these platforms without software support, though. A few years ago, that would have been a challenge because few commercial vendors supported their data center products on the Arm architecture. Today, many of the biggest open-source and proprietary projects and distributions run on Arm chips, including Red Hat Enterprise Linux, Ubuntu, Suse, VMware, MySQL, OpenStack, Docker, Microsoft .Net, DOK and OPNFV. “We have lots of support across the space,” said Henry. “And then as you go down to that tier of languages and libraries and compilers, that’s a very large investment area for us at Arm. One of our largest investments in engineering is in software and working with the software communities.”
And as Henry noted, AWS also recently launched its Arm-based servers — and that surely gave the industry a lot more confidence in the platform, given that the biggest cloud supplier is now backing it, too.
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Arm acquires data management service Treasure Data to bolster its IoT platform
Arm, the semiconductor firm you probably still remember as ARM, today announced that it has acquired Treasure Data, a data management platform for large enterprise customers. The companies didn’t announce the financial details of the transaction, but earlier reporting by Bloomberg pegged the price at $600 million.
This move strengthens Arm’s IoT nascent play, given that Treasure Data’s specialty is dealing with the large streams of data that these systems produce (as well as data from CRM, e-commerce systems and other third-party services).
This move follows Arm’s recent acquisition of Stream and indeed, the company calls the acquisition of Treasure Data “the final piece” of its “IoT enablement puzzle.” The result of this completed puzzle is the Arm Pelion IoT Platform, which combines Stream, Treasure Data and the existing Arm Mbed Cloud into a single solution for connecting and managing IoT devices and the data they produce.
Arm says Treasure Data will continue to operate as before and continue to serve new clients as well as its existing users. “It will remain an important part of industry IoT enablement, providing the ability to harness new, complex edge and device data within a comprehensive customer profile to personalize their products and improve their experiences,” the company says.
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Billionaires make it rain on Plenty, the indoor farming startup
SoftBank Vision Fund, the huge tech-investment vehicle helmed by Japanese billionaire Masayoshi Son, has led a $200 million investment into indoor farming startup Plenty. Joining Son are notable tech billionaires Eric Schmidt and Jeff Bezos. Plenty farms can grow anything except tree fruit and root vegetables, and produce crops at yields 530x greater than a typical field. Read More
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Softbank has completed its £24B cash acquisition of ARM Holdings
One of the biggest tech deals this year — and the biggest ever in the UK — has now closed. Today, Softbank announced that it has completed its acquisition of ARM Holdings, the semiconductor firm that it said in July it would acquire for £24 billion in cash (around $32 billion in today’s currency, $31 billion at the time of the deal), in order to make a big jump into IoT. As… Read More
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