Today I’m speaking with Mayukh Das about using neural architecture search for resource-constrained devices and about a new multi-objective reinforcement-learning based framework that he recently published called AUTOCOMET.

We’ll be covering such topics as how NAS research is done both at Samsung and at Microsoft,  the relationship between NAS and product teams, devices and the various types of constraints they expose, how to featurize hardware contexts, layer-wise latency calculations, surrogate models and the kinds of hardware-aware data they require, the current limitations of NAS, reinforcement learning and NAS, multi-objective optimization in the context of reinforcement learning, reward sparsity, reward shaping and shaping functions, primary and secondary rewards, their concept of co-regulated shaping, Q functions and the effects of potentials, AUTOCOMET, the future of NAS and other topics.

Thank you for tuning in!

To learn more about AUTOCOMET, find the paper here: https://arxiv.org/pdf/2203.15408.pdf