This strategic partnership aims to empower AI teams with powerful computing resources on-demand, enhancing their capabilities and streamlining their operations.

The collaboration combines the strengths of three industry leaders: Graphcore, renowned for its Intelligence Processing Units (IPUs) hardware; UbiOps, a powerful machine learning operations (MLOps) platform; and Gcore Cloud, known for its robust cloud infrastructure.

By leveraging these cutting-edge technologies, Gcore Cloud presents AI teams with a seamless experience, making it effortless to utilise IPUs for specific AI tasks while benefiting from UbiOps’ MLOps features such as model versioning, governance, and monitoring.

Andre Reitenbach, CEO at Gcore, commented:

“The collaboration between Gcore, Graphcore, and UbiOps brings a seamless experience for AI teams. This enables effortless utilisation of Gcore’s cloud infrastructure with Graphcore’s IPUs on the UbiOps platform. This means that users can take advantage of the exceptional computational capabilities of IPUs for their specific AI tasks. Also, users can leverage UbiOps’ out-of-the-box MLOps features such as model versioning, governance, and monitoring.

These features help teams to accelerate time to market with AI solutions, save on computing resource costs, and efficiently use them with on-demand hardware scaling. We’re thrilled about this partnership’s potential to enable AI projects to succeed and reach their goals.”

To showcase the significant advantages of IPUs over other devices, Gcore conducted benchmarking tests on three different compute resources: CPUs, GPUs, and IPUs.

Gcore trained a Convolutional Neural Network (CNN) – a model designed for image analysis – using the CIFAR-10 dataset containing 60,000 labelled images, on these devices.

The results were striking, with IPUs and GPUs significantly outperforming CPUs in training speed. Even with minimal optimisation, IPUs demonstrated a clear advantage over GPUs, enabling even shorter training times:

This collaboration offers AI teams unparalleled access to powerful hardware tailor-made for demanding AI and ML workloads.

By integrating Gcore Cloud, Graphcore’s IPUs, and UbiOps’ MLOps platform, teams can work more efficiently, cost-effectively, and scale their hardware as needed. The combined offering enables AI projects to realise their full potential, driving innovation and progress in the AI industry.

With this strategic alliance, Gcore, Graphcore, and UbiOps are poised to make advanced resources more accessible and empower AI teams worldwide to achieve their goals.

(Photo by Nathan Dumlao on Unsplash)

See also: Damian Bogunowicz, Neural Magic: On revolutionising deep learning with CPUs

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FP8 aims to provide a common format that accelerates AI development by optimising memory usage and works for both AI training and inference.

In addition to Graphcore, FP8 is supported by industry giants including NVIDIA, ARM, Intel, and Qualcomm. 

The C600 is based on Graphcore’s MK2 IPU and developers up to 560 TFLOPS of FP8 performance and 280 TFLOPS for FP16. It’s a PCIe Gen 4 dual-slot card with a thermal draw power of 185W.

“We are making the IPU available on a PCIe card in response to customer demand in markets where datacentre configurations, including rack size and power delivery, vary widely,” wrote Chen Jin, VP and Head of Engineering for Graphcore China, in a blog post.

“This highly versatile form factor enables Graphcore customers to tailor their system setup, including host server/chassis, to their exact requirements.”

The IPU packs 1,472 processing cores and is capable of running 8,832 independent program threads in parallel. Each IPU features 900MB of on-chip SRAM memory that is located adjacent to the processing cores.

Up to eight C600 cards can be connected in a single chassis using high-bandwidth IPU-Links.

The US recently increased the scope of its export restrictions on advanced chips to China and it’s not clear whether Graphcore had to adapt the C600 to comply.

While Graphcore is a British company, the US restrictions apply to companies in other countries that use American manufacturing and design tools for their operations.

In response to the export restrictions, NVIDIA said that it was slowing down its A100 GPU to continue selling the hardware in China. AMD, meanwhile, has entirely halted the sales of its MI250 GPU to the country.

Graphcore says the C600 is available now for pre-order in China and Singapore.

(Image Credit: Graphcore)

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LabGenius is currently focused on discovering new treatments for cancer and inflammatory diseases. The firm combines AI, lab automation, and synthetic biology for its potentially life-saving work.

Until now, the company has been using traditional GPUs for its workloads. LabGenius reports that switching to Graphcore’s IPUs in cloud instances from Cirrascale Cloud Services enabled its training of models to be reduced from one month to around two weeks.

“Previously we used GPUs and it took us about a month to have a functioning model of all the proteins that are out there,” said Dr Katya Putintseva, a Machine Learning Advisor to LabGenius.

“With Graphcore, we reduced the turnaround time to about two weeks, so we can experiment much more rapidly and we can see the results quicker.”

Specifically, LabGenius is using IPUs from Bristol, UK-based Graphcore to train a BERT Transformer model on a large data set of known proteins to predict masked amino acids. This, the company says, enables the model to effectively learn the basic biophysics of proteins.

“[The system] is looking across different features we could change about the molecule — from point mutations of simpler constructs to the overall composition and topology of multi-module proteins,” explained Tom Ashworth, Head of Technology at LabGenius.

“It’s making suggestions about what to design next… to learn about a change in the input and how that maps to a change in the output.”

One in two people now develop cancer in their lifetime. Current treatments often cause much suffering themselves and, while survival rates for most forms are increasing, only around 50 percent survive for ten years or more.

AI will help to find new cancer treatments that cause less suffering and greatly increase the odds of long-term survivability. However, while discovering new cancer treatments is the current focus of LabGenius, the company notes how the principles can be applied more widely to find new treatments for other horrible diseases that plague mankind.

“Graphcore has changed what we’re able to do, accelerating our model training time from weeks to days,” adds Ashworth.

“For our data scientists, that’s really transformative. They can move much more at the speed they think.”

(Photo by National Cancer Institute on Unsplash)

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The chip unveiled today, the Bow IPU, is the world’s first processor to be based on TSMC’s Wafer-on-Wafer (WoW) technology.

“TSMC has worked closely with Graphcore as a leading customer for our breakthrough SoIC-WoW solution as their pioneering designs in cutting-edge parallel processing architectures make them an ideal match for our technology,” said Paul de Bot, GM of TSMC Europe. 

WoW bonds two wafers together to generate a new 3D die:

• The first wafer is for AI processing and is architecturally compatible with the GC200 IPU processor. It has 1,472 independent IPU-Core tiles, is capable of running more than 8,800 threads, and has 900MB of in-processor memory.
• The second wafer is a power delivery die and features deep trench capacitors that enable a large performance increase thanks to being located right next to the processing cores and memory.

Compared to its predecessors, Graphcore claims that its Bow IPU offers up to 40 percent higher performance and 16 percent better power efficiency for real-world AI applications.

In terms of power, Graphcore says the flagship Bow Pod delivers more than 89 petaFLOPS of AI compute. The superscale Bow POD ups that to an incredible 350 petaFLOPS.

Here’s how that power translates into real-world performance across popular AI applications:

Ultra-intelligence AI supercomputer

The most interesting announcement made by Graphcore today is of its plan to develop an “ultra-intelligence AI supercomputer”.

Graphcore points to how the human brain has “approximately 100 billion neurons and more than 100 trillion parameters in a biological-neural-network system that delivers a level of compute yet to be matched by any silicon computers.”

Graphcore says that it’s developing an AI computer that will surpass the parametric capacity of the brain.

The computer will be called the ‘Good’ computer, named after computer science pioneer Jack Good (born Isadore Jacob Gudak).

The achievements of Jack Good – including his pivotal work during the Second World War – are worth reading up on. However, for the purposes of this story, what’s most notable is the fact Good was the first person to describe a machine that is more powerful than a human brain in his 1965 paper Speculations Concerning the First Ultra-Intelligent Machine.

“Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever,” wrote Good.

“Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an ‘intelligence explosion,’ and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make.”

By 2024, Graphcore expects to have delivered the first ultra-intelligence AI computer.

The computer will feature:

• Over 10 exaFLOPS of AI floating point compute
• Up to four petabytes of memory with a bandwidth of over 10 petabytes/second
• Support for AI model sizes of 500 trillion parameters 
• 3D wafer on wafer logic stack
• Fully supported by Graphcore’s Poplar® SDK 
• Expected cost: ~$120 million (configuration dependent) 

Graphcore says it will provide further updates about the Good computer over the coming year.

(Imagery Credit: Graphcore)

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Bristol-based Graphcore ranked number one on Fast Company’s top 10 most innovative AI companies of 2020 list. Nvidia, for comparison, ranked fifth.

Fast Company’s confidence in Graphcore clearly isn’t misplaced. Announcing its GC200 processor, Graphcore says its new chip is the world’s most complex.

The GC200 processor boasts 59.4 billion transistors and takes the crown from Nvidia’s A100 as the world’s largest. The A100 was announced by Nvidia earlier this year and features 54 billion transistors.

Each GC200 chip has 1,472 independent processor cores and 8,832 separate parallel threads, all supported by 900MB of in-processor RAM.

Graphcore says that up to 64,000 of the 7nm GC200 chips can be linked to create a massive parallel processor with around 16 exaflops of computational power and petabytes of power. Such a system would be able to support AI models with trillions of parameters.

“We are impressed with Graphcore’s technology for energy-efficient construction and execution of large, next-generation ML models, and we expect significant performance gains for several of our AI-oriented research projects in medical imaging and cardiac simulations,” comments Are Magnus Bruaset, Research Director at Simula Research Laboratory.

“We are also pursuing other avenues of research that can push the envelope for Graphcore’s multi-IPU systems, such as how to efficiently conduct large-scale, sparse linear algebra operations commonly found in physics-based HPC workloads.”

The GC200 is just the second chip to be launched by Graphcore. Compared to the first generation, the GC200 delivers an up to 9.3x performance increase.

Graphcore’s founders believe the IPU approach that the company is taking is more efficient than Nvidia’s GPU route. The ability to scale up to thousands of IPU processors in existing compute infrastructures could mean that the cost could be 10-20x lower than using GPUs.

Back in February, Graphcore announced that it had raised $150 million in funding for its R&D. The company’s total valuation is $1.95 billion.

Graphcore was fortunate to have secured its cash before the COVID-19 pandemic really hit – with many startups reporting difficulties obtaining vital funding where there was previous interest. Undoubtedly, the GC200 will help to power research to get us through this pandemic and all the other challenges the world faces now and in the future.

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Sequoia Capital has previously invested in companies such as Apple, Google, and WhatsApp. The investor has now led a $50m (£38.2m) funding round in Graphcore — which has its headquarters in Bristol, UK.

Dedicated AI chips are set to boom in the coming years with implementation in smartphones, connected vehicles, IoT devices, and more. Back in September, we posted an editorial analysing the extent of which they’re about to take off.

In the piece, we highlighted the excitement from investors. Chip giant Nvidia has been moving into AI and the revenue from this side of the business has more than made up for its shortfall in gaming — which is often cyclical in nature.

“AI is the most important technology development of our time, with the greatest potential to help society,” says Nvidia Corp. Chief Executive Jensen Huang. “As the world’s leading cloud providers deploy the world’s best AI platform, with Volta GPUs and Nvidia software, we’ll see amazing breakthroughs in medicine, autonomous transportation, precision manufacturing and much more.”

As a result, of any business in S&P 500 Index, Nvidia had the largest stock increase in 2016. The company’s shares increased 227 percent over the previous year.

“Machine intelligence will cause an explosion of new applications and services that will transform every industry,” comments Matt Miller, partner at Sequoia, who will join the Graphcore board of directors. “The unique nature of this workload and scale of this opportunity creates an opening where a valuable new chip company can be created.”

Graphcore is reaping the benefits of the appetite for dedicated AI chips. This latest round of funding is in addition to a previous in July from some of the world’s leading AI experts — including the founder of Cambridge-based DeepMind, and Uber’s chief scientist.

“Efficient AI processing power is rapidly becoming the most sought after resource in the technological world,” says Nigel Toon, CEO at Graphcore. “The performance of Graphcore’s processor, compared to other accelerators, is going to be transformative whether you are a medical researcher, roboticist, online marketplace, social network, or building autonomous vehicles.”

We’re already seeing adoption of dedicated AI chips in smartphones. Huawei is making a promising start with the Mate 10 Pro and Google is releasing a dedicated Android Neural Network API early next year as part of its vision to build an open AI ecosystem.

The next year is going to be exciting as the adoption of AI chips accelerates. We’ll keep you updated with all developments.

What are your thoughts on dedicated AI chips? Let us know in the comments.

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