Although advanced neural networks continue to dramatically improve the capabilities of artificial intelligence systems, they are associated with substantial energy use. In an effort to address this problem a growing number of organisations are focused on the creation of technologies designed to reduce energy use in the training and operation of such systems.
As Dr. Zhangyang 'Atlas' Wang, Assistant Professor of Electrical and Computer Engineering at The University of Texas at Austin, explains, the Early Bird Ticket algorithm leverages an important recent finding called the lottery ticket hypothesis – which describes how a dense and randomly initialised deep neural network (DNN) has a small but critical subnetwork, known as a ‘winning ticket,’ that can be ‘trained alone to achieve a comparable accuracy to the former in a similar number of iterations.’
