  The state-of-the-art hardware platforms for training Deep Neural Networks
(DNNs) are moving from traditional single precision (32-bit) computations
towards 16 bits of precision -- in large part due to the high energy efficiency
and smaller bit storage associated with using reduced-precision
representations. However, unlike inference, training with numbers represented
with less than 16 bits has been challenging due to the need to maintain
fidelity of the gradient computations during back-propagation. Here we
demonstrate, for the first time, the successful training of DNNs using 8-bit
floating point numbers while fully maintaining the accuracy on a spectrum of
Deep Learning models and datasets. In addition to reducing the data and
computation precision to 8 bits, we also successfully reduce the arithmetic
precision for additions (used in partial product accumulation and weight
updates) from 32 bits to 16 bits through the introduction of a number of key
ideas including chunk-based accumulation and floating point stochastic
rounding. The use of these novel techniques lays the foundation for a new
generation of hardware training platforms with the potential for 2-4x improved
throughput over today's systems.
