IBM to slash time needed to train AI with new resistive processing tech

Tech giant IBM has developed a new technology that can speed up the training for deep neural networks (DNNs). Though DNNs can be taught to perform almost any task, training them is time consuming and complex. Training artificial intelligence (AI) systems involves the usage of supercomputers or data centres for a significant number of days. In a research paper titled'Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices', authors Tayfun Gokmen and Yurii Vlasov said:"In recent years, DNNs have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. "Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days," But the scientists at IBM's T.J. Watson Research Center have come out with "resistive processing units," that can reduce the horsepower and learning time sharply. "We proposed a concept of resistive processing unit (RPU) devices that can simultaneously store and process data locally and in parallel, thus potentially providing significant acceleration for DNN training.