Nvidia to build UK’s largest supercomputer for AI research
The computer, which will be the UK’s most powerful, is to be known as “Cambridge-1” and be activated by the end of the year. Nvidia said it would be the 29th most powerful and among the top 3 most efficient supercomputers in the world, with potential uses including investigating COVID-19.
In , Jensen Huang, founder and CEO of Nvidia, said: “Tackling the world’s most pressing challenges in healthcare requires massively powerful computing resources to harness the capabilities of AI. 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.”#
Researchers from organisations including GSK, AstraZeneca, Guy’s and St Thomas’ NHS Foundation Trust, King’s College London and Oxford Nanopore Technologies are slated to make use of the technology.
Matt Hancock, the UK’s Secretary of State for Health and Social Care, said: “Today’s announcement from NVIDIA is an exciting moment for the U.K.’s world-leading healthcare industry and a tremendous vote of confidence in the U.K. as an international centre for research, AI and innovation. Accelerating drug discovery has never been so important and it is investments like this that can make a real difference in our fight against countless diseases. I care about technology because I care about people and NVIDIA’s new supercomputer will aid the U.K.’s best and brightest to undertake research that will save lives.”
One possible underlying motivation for the announcement is as a sweetener for its purchase of ARM, which has and may still be blocked. Criticism of the $40bn purchase has been based around concerns that Arm's status as a British technology powerhouse will be eroded, with the potential for the loss of jobs in the UK, though Nvidia insists this isn’t the case.
What is neuromorphic AI?
AI is dead. Long live AI?
AI is evolving. The first generation of machine learning used ordinary logic and rules to draw conclusions in a very specific manner. A good example would be IBM’s Deep Blue computer, which was trained to play chess to championship standard. That hasn’t disappeared, but it has been augmented by more perceptive deep learning networks that can analyze a broader set of parameters and provide intelligent insights.
And neuromorphic AI is next?
Correct. Neuromorphic computing is a way of designing hardware – microprocessors, really – to work more like human brains. The idea is that this new iteration of AI hardware will allow machine learning of the future to deal better with ambiguity and contradiction, things that are currently difficult to process for computers.
How does neuromorphic AI work?
The problem with current chip architecture is that it is not very efficient. Because of the linearity of the process, the chips have to built with a massive amount of horsepower just in case it’s needed. Building a human brain that way would be unfeasible, so engineers have had to rethink the nature of chip design in their quest to get computers to perform more of the tasks human brains are good at. Enter SNNs.
What’s an SNN?
A spiking neural network (SNN) is, in the words of chipmaker Intel, “a novel model for arranging those elements to emulate natural neural networks that exist in biological brains.” Each ‘neuron’ fires independently, triggering other neurons only when they are required. Intel again: “By encoding information within the signals themselves and their timing, SNNs simulate natural learning processes by dynamically remapping the synapses between artificial neurons in response to stimuli.”