Integer-Only Inference for Deep Learning in Native C

Tutorial: converting a deep neural network for deployment on low-latency, low-compute devices via uniform quantization and the fixed-point representation.

Integer-only inference allows for the compression of deep learning models for deployment on low-compute and low-latency devices. Many embedded devices are programmed using native C and do not support floating-point operations and dynamic allocation. Nevertheless, small deep learning models can be deployed to such devices with an integer-only inference pipeline through uniform quantization and the fixed-point representation.

We employed these methods …

deep learning embedded fixed-point learning quantization