Graphcore

For most organisations, the process of cognification is the single biggest challenge of the next five years. Everything will get smarter – in theory. The limitations of existing computer chips, however, is slowing down the process. Put simply, today’s technology simply isn’t up to the job.

“What we heard universally was that current hardware was holding developers back,” says Nigel Toon, co-founder of Graphcore, the Bristol-based startup behind a new chip to help speed up the process-hogging, resource-intensive deployment of AI.


By using cloud computing and vast datasets, some neural networks function sufficiently well. The more powerful AI systems in development, however, struggle to process complex rapid-fire calculations at speed if using computer processing units (CPUs) which work sequentially. Latency, in other words, has slowed.

“For 70 years we have programmed computers to work on instructions step-by-step,” says Toon, 54. AI, however, involves computers learning and adapting from the data they process. Speech is simple enough to understand and can be handled by existing technology. Understanding entire languages and the context in which words are said is more difficult, requiring systems to store data as they go and to delve deep into their memory to understand the background to conversations. “The compute required to learn from data is very different to the traditional process. It’s a completely different type of workload,” says Toon.

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Stopgap solutions – including putting the CPU in the cloud to share the workload and using graphics processing units (GPUs) – aren’t fast enough for the rapidly advancing world of AI. Google, Amazon and Apple are already working on hardware to solve this, prompting a flood of VC capital into previously unfashionable chip startups.

Toon’s prior experience – he and co-founder Simon Knowles launched semiconductor company Icera in 2002, later selling it to chip-maker Nvidia for $435 million (£315 million) in 2011 – inspired him to think about the hardware limits artificial intelligence is butting up against.


In 2016, Toon and Knowles met researchers to learn about their frustrations and future plans. The pair decided to work from first principles, thinking less about code and more about the computer itself. Their solution required building an entirely new type of processor – and thinking about computer workloads in a different way.

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Ordinarily, CPUs solve problems by collecting blocks of data, then running algorithms or logic operations on that information in sequence. Modern quad-core chips have four parallel processors. GPUs, designed for gaming, have parallel processors that can perform multiple tasks at the same time. With AI systems, computers need to pull huge amounts of data in parallel from various locations, then process it quickly. This process is known as graph computing, which focuses on nodes and networks rather than if-then instructions.

Graphcore’s new chip, an intelligence processing unit (IPU), emphasises graph computing with massively parallel, low-precision floating-point computing. It has more than 1,000 processors which communicate with each other to share the complex workload required for machine learning.

“The architecture of the hardware is quite simple and straightforward,” says Toon. The difference comes in how the individual processors on the chip communicate with each other and external memory – powered through Poplar, Graphcore’s proprietary software. “You can’t simply come up with the hardware, and then try to figure out how to write the software with it,” says Toon.


Poplar moves data across its chip more efficiently, meaning less wasted processing power. It also does so at the right time, using all the processors in sequence. The performance improvements are significant: when compared to today’s most powerful GPUs, Graphcore’s chip can handle advanced AI algorithms up to ten times faster, Toon claims. But it’s in the future that Graphcore will come into its own: the software can be fine-tuned to eke out further improvements as required.

Graphcore claims its data handling and processing architecture will be up to 100 times more efficient than the most powerful GPU. “It’s going to open up new opportunities and applications for people,” says Toon.

The company’s supercharged chip already has plenty of interest: Sequoia Capital, the VC firm which has previously invested in Google and Apple, placed $50 million of cash into the project to help it grow. “It’s not something that happens every day,” Toon admits. Despite that, Graphcore isn’t content to stand still. “As we get access to the next generation of silicon technology, and as Moore’s law continues, we can get more and more transistors into a smaller space. We’ll see people making new breakthroughs thanks to AI.”