Energy-performance design exploration of a low-power microprogrammed deep-learning accelerator
Giulia Santoro, Mario R. Casu, Valentino Peluso, Andrea Calimera, Massimo Alioto · 2018
This paper presents the design space exploration of a novel microprogrammable accelerator in which PEs are connected with a Network-on-Chip and benefit from low-power features enabled through a practical implementation of a Dual-Vddassignment scheme. An analytical model, fitted with postlayout data obtained with a 28nm FDSOI design kit, returns implementations with optimal energy-performance tradeoff by taking into consideration all the key design-space variables. The obtained Pareto analysis helps us infer optimization rules aimed at improving quality of design.