  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. Finally, we describe an
efficient hardware accelerator that can take advantage of the proposed low
precision numerics.
