PredJoule: A Timing-Predictable Energy Optimization Framework for Deep Neural Networks
Soroush Bateni, Husheng Zhou, Yuankun Zhu, Cong Liu · 2018
The revolution of deep neural networks (DNNs) is enabling dramatically better autonomy in autonomous driving. However, it is not straightforward to simultaneously achieve both timing predictability (i.e., meeting job latency requirements) and energy efficiency that are essential for any DNN-based autonomous driving system, as they represent two (often) conflicting goals. In this paper, we propose PredJoule, a timing-predictable energy optimization framework for running DNN workloads in a GPU-enabled automotive system. PredJoule achieves both latency guarantees and energy efficiency through a layer-aware design that explores specific performance and energy characteristics of different layers within the same neural network. We implement and evaluate PredJoule on the automotive-specific NVIDIA Jetson TX2 platform for five state-of-the-art DNN models with both high and low variance latency requirements. Experiments show that PredJoule rarely violates job deadlines, and can improve energy by 65% on average compared to five existing approaches and 68% compared to an energy-oriented approach.