Co-Optimization of DNN and Hardware Configurations on Edge GPUs

Abstract

The ever-increasing complexity of both Deep Neural Networks (DNN) and hardware accelerators has made the co-optimization of these domains extremely complex. Previous works typically focus on optimizing DNNs given a fixed hardware configuration or optimizing a specific hardware architecture given a fixed DNN model. Recently, the importance of the joint exploration of the two spaces drew more and more attention. Our work targets the co-optimization of DNN and hardware configurations on edge GPU accelerators. We propose an evolutionary-based co-optimization strategy by considering three metrics: DNN accuracy, execution latency, and power consumption. By combining the two search spaces, a larger number of configurations can be explored in a short time interval. In addition, a better tradeoff between DNN accuracy and hardware efficiency can be obtained. Experimental results show that the co-optimization outperforms the optimization of DNN for fixed hardware configuration with up to 53% hardware efficiency gains with the same accuracy and inference time.