IBM Invents 'Resistive' Chip That Can Speed Up AI Training By 30,000x

IBM researchers, Tayfun Gokmen and Yurii Vlasov, unveiled a paper in which they invented the concept for a new chip called a Resistive Processing Unit (RPU) that can accelerate Deep Neural Networks training by up to 30,000x compared to conventional CPUs. A Deep Neural Network (DNN) is an artificial neural network with multiple hidden layers that can be trained in an unsupervised or supervised way, resulting in machine learning (or artificial intelligence) that can "learn" on its own. This is similar to what Google's AlphaGo AI has been using to learn playing Go. AlphaGo used a combination of a search-tree algorithm and two deep neural networks with multiple layers of millions of neuron-like connections. One, called the "policy network," would calculate which move has the highest chance of helping the AI win the game, and another one, called the "value network," would estimate how far it needs to predict the outcome of a move before it has a high enough chance to win in a localized battle. Many machine learning researchers have begun focusing on deep neural networks because of their promising potential.