AI Neural Networks Could Soon Train on Smartphones

Deep learning is notorious for being an energy-intensive field that sees its applications limited. But what if these models could be run with higher energy efficiency? That is a question many researchers have asked, and a new team from IBM may have found an answer. New research being presented this week at NeurIPS (Neural Information Processing Systems -- the biggest annual AI research conference) showcases a process that could soon reduce the number of bits needed to represent data in deep learning from 16 down to four without the loss of accuracy. "In combination with previously proposed solutions for 4-bit quantization of weight and activation tensors, 4-bit training shows a non-significant loss in accuracy across application domains while enabling significant hardware acceleration ( 7 over state of the art FP16 systems)," write the researchers in their abstract.