Hardware-aware optimization refers to the process of optimizing the performance of deep learning models by maximizing the use of the target hardware. AI teams can achieve this by employing the right algorithms and co-designing both hardware and software in the development of the architecture of a specific use case. It can also involve the use of AutoNAC, which is an algorithmic acceleration technology that is hardware-aware and works on top of other optimization techniques.
