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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