  Designing suitable deep model architectures, for AI-driven on-device apps and
features, at par with rapidly evolving mobile hardware and increasingly complex
target scenarios is a difficult task. Though Neural Architecture Search
(NAS/AutoML) has made this easier by shifting paradigm from extensive manual
effort to automated architecture learning from data, yet it has major
limitations, leading to critical bottlenecks in the context of mobile devices,
including model-hardware fidelity, prohibitive search times and deviation from
primary target objective(s). Thus, we propose AutoCoMet that can learn the most
suitable DNN architecture optimized for varied types of device hardware and
task contexts, ~ 3x 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.
