Hardware-Aware Neural Architecture Search with Mixed-Precision Quantization

Incorporating mixed-precision quantization in Hardware-Aware Neural Architecture Search (HW-NAS) is a promising research field gaining relevancy the more deep learning models are deployed in embedded applications. However, the prevalent approach, a multi-stage optimization pipeline of NAS, Pruning and Quantization, does not consider dependencies between architectural decisions and quantization policies which could result in suboptimal compressed models. Although, there is research combining quantization, pruning and coarse architectural decisions in a NAS search space with SOTA results [1]- [3], further work has to be done to improve on those search results, as well as the search time. Furthermore, NAS approaches have to be tested in real-life application cases to a greater extent, as most have been evaluated only on proxy metrics such as the number of FLOPS, which is not a meaningful metric for every hardware platform.