To further improve the efficiency and performance of SWAP-NAS in future iterations, several strategies can be considered:
Enhanced Regularisation: Fine-tuning regularisation parameters µ and σ based on empirical observations from previous searches could help optimize model size control more effectively.
Parallel Processing: Implementing parallel processing techniques could speed up architecture evaluations simultaneously across multiple GPUs or CPU cores.
Dynamic Hyperparameter Tuning: Incorporating dynamic hyperparameter tuning mechanisms within SWAP-NAS could adaptively adjust search parameters during runtime based on performance feedback.
Transfer Learning: Leveraging transfer learning techniques by initializing with pre-trained models or knowledge from previous searches could accelerate convergence and enhance overall search efficiency.
Ensemble Methods: Utilizing ensemble methods by combining multiple diverse candidate solutions generated throughout the search process may lead to improved final architecture selections with enhanced generalization capabilities.
By integrating these advanced strategies into future iterations of SWAP-NAS, it is possible to achieve even higher levels of efficiency and performance in neural architecture search tasks while addressing potential limitations encountered in current implementations.