AutoCoMet: Smart Neural Architecture Search via Co-Regulated Shaping Reinforcement

Designing suitable deep model architectures for AI-driven on-device apps and features, at par with evolving mobile hardware and complex target scenarios, is difficult. Though Neural Architecture Search (NAS/AutoML) has made this easier by automated architecture learning from data saving substantial manual effort, yet it has major limitations, in context of mobile devices, including model-hardware alignment, prohibitive search times and divergence from primary target objective(s). So, we propose AUTOCOMET that can learn the most suitable DNN architecture optimized for varied types of device hardware and task contexts, ≈ 3× faster. Our novel co-regulated shaping reinforcement controller together with the high fidelity hardware meta-behavior predictor produces a smart, fast NAS framework that adapts to context via a generalized formalism for any kind of multi-criteria optimization.