Neural Architecture Search (NAS) is the process of automating network architecture engineering or finding the optimal design of artificial neural networks. The methods for NAS include three components: the search space, search strategy, and performance estimation strategy. It is a subfield of automated machine learning (AutoML). AutoNAC has a NAS component that redesigns a given trained model’s architecture to optimally improve its inference performance (throughput, latency, memory, etc.) for specific target hardware while preserving its baseline accuracy.