An Empirical Study of Low Precision Quantization for TinyML. (arXiv:2203.05492v1 [cs.LG])

Tiny machine learning (tinyML) has emerged during the past few years aiming
to deploy machine learning models to embedded AI processors with highly
constrained memory and computation capacity. Low precision quantization is an
important model compression technique that can greatly reduce both memory
consumption and computation cost of model inference. In this study, we focus on
post-training quantization (PTQ) algorithms that quantize a model to low-bit
(less than 8-bit) precision with only a small set of calibration data and
benchmark …

arxiv precision quantization study tinyml