  In this paper, we focus on temperature-aware Monolithic 3D (Mono3D) deep
neural network (DNN) inference accelerators for biomedical applications. We
develop an optimizer that tunes aspect ratios and footprint of the accelerator
under user-defined performance and thermal constraints, and generates
near-optimal configurations. Using the proposed Mono3D optimizer, we
demonstrate up to 61% improvement in energy efficiency for biomedical
applications over a performance-optimized accelerator.
