Inference Energy Analysis in Context of Hardware-Aware NAS

Hardware-aware neural architecture search (HW-aware NAS) methods are crucial for designing and optimizing convolutional neural networks (CNNs) and their efficient deployment on hardware accelerators. In this work, we analyze two HW-aware NAS methods, EvoApproxNAS and ApproxDARTS, and investigate the impact of precise hardware parameter measurement on the performance of resulting CNNs. In particular, we compare the precise measurement of inference energy using the extended Timeloop tool with the original approach employed by EvoApproxNAS and ApproxDARTS, which relies on a simple analytical energy estimation based on the number of multiplications performed during the inference. The analysis demonstrates how improved energy measurements can enhance the search process of HW-aware NAS methods, resulting in more energy-efficient architectures. Our results show that without precise hardware parameter measurement, the HW-aware NAS can produce acceptable results but may fail to fully exploit the potential of hardware accelerator, especially if the 8×N -bit approximate multipliers are implemented in convolutional layers.