  Deep learning is finding its way into high energy physics by replacing
traditional Monte Carlo simulations. However, deep learning still requires an
excessive amount of computational resources. A promising approach to make deep
learning more efficient is to quantize the parameters of the neural networks to
reduced precision. Reduced precision computing is extensively used in modern
deep learning and results to lower execution inference time, smaller memory
footprint and less memory bandwidth. In this paper we analyse the effects of
low precision inference on a complex deep generative adversarial network model.
The use case which we are addressing is calorimeter detector simulations of
subatomic particle interactions in accelerator based high energy physics. We
employ the novel Intel low precision optimization tool (iLoT) for quantization
and compare the results to the quantized model from TensorFlow Lite. In the
performance benchmark we gain a speed-up of 1.73x on Intel hardware for the
quantized iLoT model compared to the initial, not quantized, model. With
different physics-inspired self-developed metrics, we validate that the
quantized iLoT model shows a lower loss of physical accuracy in comparison to
the TensorFlow Lite model.
