August 9, 2017 Nicole Hemsoth
The golden grail of deep learning has two handles. On the one hand, developing and scaling systems that can train ever-growing model sizes is one concern. And on the other side, cutting down inference latencies while preserving accuracy of trained models is another issue.
Being able to do both on the same system represents its own host of challenges, but for one group at IBM Research, focusing on the compute-intensive training element will have a performance and efficiency trickle-down effect that speed the entire deep learning workflow—from training to inference. This work, which is being led at the T.J. Watson …Read more