Architectural Design Model Guided On-Demand Power Management of Energy-Efficient GPGPU for SLAM

Abstract

Simultaneously localization and mapping (SLAM) is a core component in many embedded domains, e.g., robots, augmented and virtual reality. Due to SLAM’s high demand on computation resources, general-purpose graphic processing units (GPGPUs) are often used as its processing engine. Meanwhile, embedded systems usually have strict power constraint. Thus, how to deliver required performance for SLAM, yet still meet the power limit, is a great challenge faced by GPGPU designer. In this work, we discover the general principles of designing energy-efficient GPGPU for SLAM as “many SMs, enough SPs and registers, small caches”, by analyzing the implication of individual design parameters on both performance and power. Then, we conduct large-scale design space exploration and fit the Pareto frontier with a two-term exponential model. Further, we construct gradient boosting decision tree (GBDT)-based design models to predict the performance and power given the design parameters. The evaluation shows that our GBDT-based models can achieve [Formula: see text]3% mean average percentage error, which significantly outperform other machine learning models. With these models, a kernel’s requirement on hardware resources can be well understood. Based on such knowledge, we introduce design model guided power management strategies, including power gating and dynamic frequency and voltage scaling (DFVS). Overall, by combining these two power management strategies, we can improve the energy delay product by 36%.