Inference Energy Analysis in Context of Hardware-Aware NAS

Hardware-aware neural architecture search (HW-aware NAS) methods are crucial for designing and optimizing deep neural networks (DNNs) for 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 parameters (such as energy) measurement using Timeloop on their performance.
In particular, we compare this precise measurement approach with the original approach employed by EvoApproxNAS and ApproxDARTS, which relied on a simple analytical energy estimation based on the number of multiplications performed during the inference phase of the convolutional neural network (CNN). Our analysis demonstrates how the improved energy measurements enhance the search process of HW-aware NAS methods, resulting in more energy-efficient architectures.
Furthermore, we highlight the importance of precise hardware parameters measurement, showing that accurate hardware modeling is critical for obtaining CNNs with good accuracy-energy trade-offs. 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 8xN-bit approximate multipliers are considered, ultimately limiting the efficiency of designed architectures.