MOHAQ: Multi-Objective Hardware-Aware Quantization of Recurrent Neural Networks. (arXiv:2108.01192v3 [cs.LG] UPDATED)

The compression of deep learning models is of fundamental importance in
deploying such models to edge devices. The selection of compression parameters
can be automated to meet changes in the hardware platform and application using
optimization algorithms. This article introduces a Multi-Objective
Hardware-Aware Quantization (MOHAQ) method, which considers hardware efficiency
and inference error as objectives for mixed-precision quantization. The
proposed method feasibly evaluates candidate solutions in a large search space
by relying on two steps. First, post-training quantization is applied …

arxiv hardware networks neural networks