  The state-of-the-art (SOTA) for mixed precision training is dominated by
variants of low precision floating point operations, and in particular, FP16
accumulating into FP32 Micikevicius et al. (2017). On the other hand, while a
lot of research has also happened in the domain of low and mixed-precision
Integer training, these works either present results for non-SOTA networks (for
instance only AlexNet for ImageNet-1K), or relatively small datasets (like
CIFAR-10). In this work, we train state-of-the-art visual understanding neural
networks on the ImageNet-1K dataset, with Integer operations on General Purpose
(GP) hardware. In particular, we focus on Integer Fused-Multiply-and-Accumulate
(FMA) operations which take two pairs of INT16 operands and accumulate results
into an INT32 output.We propose a shared exponent representation of tensors and
develop a Dynamic Fixed Point (DFP) scheme suitable for common neural network
operations. The nuances of developing an efficient integer convolution kernel
is examined, including methods to handle overflow of the INT32 accumulator. We
implement CNN training for ResNet-50, GoogLeNet-v1, VGG-16 and AlexNet; and
these networks achieve or exceed SOTA accuracy within the same number of
iterations as their FP32 counterparts without any change in hyper-parameters
and with a 1.8X improvement in end-to-end training throughput. To the best of
our knowledge these results represent the first INT16 training results on GP
hardware for ImageNet-1K dataset using SOTA CNNs and achieve highest reported
accuracy using half-precision
