WRPN & Apprentice: Methods for Training and Inference using Low-Precision Numerics

Understand

Today's high performance deep learning architectures involve large models with numerous parameters.

• Low precision numerics has emerged as a popular technique to reduce both the compute and memory requirements of these large models.
• However, lowering precision often leads to accuracy degradation.
• We describe three schemes whereby one can both train and do efficient inference using low precision numerics without hurting accuracy.