Enabling Hard Constraints in Differential Neural Network and Accelerator Co-Search
TimeWednesday, July 13th1:30pm - 1:52pm PDT
Location3005, Level 3
Event Type
Research Manuscript
AI/ML Design: System and Platform
DescriptionCo-exploration of an optimal neural architecture and its hardware accelerator is an approach of rising interest which addresses the computational cost problem, especially in low-profile systems.
The difficulty of having to search the large co-exploration space is often addressed by adopting the idea of differentiable neural architecture search.
Despite the superior search efficiency of the differentiable co-exploration, it faces a critical challenge of not being able to systematically satisfy hard constraints, such as frame rate or power budget.
To handle the hard constraint problem of differentiable co-exploration, we propose ConCoDE,
which searches for hard-constrained solutions without compromising global design objectives.