Post-Training Quantization for Energy Efficient Realization of Deep Neural Networks. (arXiv:2210.07906v1 [cs.LG])

The biggest challenge for the deployment of Deep Neural Networks (DNNs) close
to the generated data on edge devices is their size, i.e., memory footprint and
computational complexity. Both are significantly reduced with quantization.
With the resulting lower word-length, the energy efficiency of DNNs increases
proportionally. However, lower word-length typically causes accuracy
degradation. To counteract this effect, the quantized DNN is retrained.
Unfortunately, training costs up to 5000x more energy than the inference of the
quantized DNN. To address this …

arxiv energy energy efficient networks neural networks quantization training