The humble GPU is taking over the world one bit at a time.

Having just returned from the NVIDIA’s GTC conference in Amsterdam last week, my mind is still buzzing from the implications of the Keynote by CEO Jen-Hsun Huang.

While dedicated video processing cards have been used in arcade machines since the 70’s it was NVIDIA that popularised the term GPU or ‘graphics processing unit’ in the 90’s, when they started promoting them to specifically render graphics and process video information in computers.

Since then, GPU technology has advanced at a phenomenal rate due to huge leaps in chip technology coupled with an equally huge appetite for higher and higher graphics and video rendering quality and performance.

With staggering rates of GPU evolution of around 65x just in the last four years, scales like Moore's law are completely blown out the water.

But as I came to discover last week, this recent acceleration is not simply due to people’s desire for graphical processing but also data or information processing. The applications of a graphics card that can process massive amounts of information in parallel have quietly been put to work paving the way for nascent industries in Deep learning, Artificial intelligence (AI) and Virtual Reality.

The humble graphics processing unit has indeed come of age, with things like: haptic or touch feedback, acoustic modelling and deep learning,  now all potentially handled ‘under the hood’, the very descriptor ‘GPU’ will soon seem as quaint as ‘Carphone Warehouse’. Whether the term survives is another matter, but what is clear is that graphics and video processing are no longer the main focus of the very companies that set out to improve them.

Huang entitled his lengthy Key-note ‘The Deep Learning AI Revolution’ and explained that with milestones in data, image and audio processing having been reached, the stage was set for machines to learn by themselves with very  little guidance from us.

The Head of NVIDIA went on to set out the four pillars of machine learning.

1.    Training – Processing billions of trillions of bits of data which feeds into…

2.    A Deep neural network - with hundreds of hidden layers running billions of operations which feed…

3.    Inferencing Datacenters for fast response to 10’s of billions of written, voice, image and video queries each day, which have a two-way link to…

4.    Inferencing Devices or intelligent devices that are all around us, in our homes, cars, cities and workplaces.

If you were in any doubt about the importance of the latest GPU or an Internet-enabled world of ‘things’, prepare for your world to be turned upside down.

Realtime analysis of what is going on in the world from millions of YouTube clips, conversation analysis, CCTV analysis, IOT and traffic analysis, mobile usage analysis, - the list is endless - means computers will have a far better idea of what is going on than any single person, organisation or government in the world.  

In the future, those that control and query this data will be those with ultimate power. What we do with all this knowledge and data… well, that’s another thing.