  Conventional stochastic rounding (CSR) is widely employed in the training of
neural networks (NNs), showing promising training results even in low-precision
computations. We introduce an improved stochastic rounding method, that is
simple and efficient. The proposed method succeeds in training NNs with 16-bit
fixed-point numbers and provides faster convergence and higher classification
accuracy than both CSR and deterministic rounding-to-the-nearest method.
