Oct
05
2020
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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.”

 

Oct
08
2019
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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.

arm instructions

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.

custom assemblerFor 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.

Feb
20
2019
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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.

ARM NEOVERSE N1

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.

Aug
02
2018
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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.

Sep
05
2016
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Softbank has completed its £24B cash acquisition of ARM Holdings

IOTGlobe 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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