1 research outputs found
Quantization Error as a Metric for Dynamic Precision Scaling in Neural Net Training
Recent work has explored reduced numerical precision for parameters,
activations, and gradients during neural network training as a way to reduce
the computational cost of training (Na & Mukhopadhyay, 2016) (Courbariaux et
al., 2014). We present a novel dynamic precision scaling (DPS) scheme. Using
stochastic fixed-point rounding, a quantization-error based scaling scheme, and
dynamic bit-widths during training, we achieve 98.8% test accuracy on the MNIST
dataset using an average bit-width of just 16 bits for weights and 14 bits for
activations, compared to the standard 32-bit floating point values used in deep
learning frameworks