Accelergy: An Architecture-Level Energy Estimation Methodology for Accelerator Designs

With Moore's law slowing down and Dennard scaling ended, energy-efficient domain-specific accelerators, such as deep neural network (DNN) processors for machine learning and programmable network switches for cloud applications, have become a promising way for hardware designers to continue bringing energy efficiency improvements to data and computation-intensive applications. To ensure the fast exploration of the accelerator design space, architecture-level energy estimators, which perform energy estimations without requiring complete hardware description of the designs, are critical to designers. However, it is difficult to use existing architecturelevel energy estimators to obtain accurate estimates for accelerator designs, as accelerator designs are diverse and sensitive to data patterns. This paper presents Accelergy, a generally applicable energy estimation methodology for accelerators that allows design specifications comprised of user-defined high-level compound components and user-defined low-level primitive components, which can be characterized by third-party energy estimation plug-ins. An example with primitive and compound components for DNN accelerator designs is also provided as an application of the proposed methodology. Overall, Accelergy achieves 95% accuracy on Eyeriss, a well-known DNN accelerator design, and can correctly capture the energy breakdown of components at different granularities.