Neural networks running on GPUs have achieved some amazing advances in artificial intelligence, but the two are accidental bedfellows. IBM researchers hope a new chip design tailored specifically to run neural nets could provide a faster and more efficient alternative.
It wasn’t until the turn of this decade that researchers realized GPUs (graphics processing units) designed for video games could be used as hardware accelerators to run much bigger neural networks than previously possible.
That was thanks to these chips’ ability to carry out lots of computations in parallel rather than having to work through them sequentially like a traditional CPU. That’s particularly useful for simultaneously calculating the weights of the hundreds of neurons that make up today’s deep learning networks.
