Evolutionary-Based Co-optimization of DNN and Hardware Configurations on Edge GPU.

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 draw more and more attention. Our work targets the co-optimization of DNN and hardware configurations on edge GPU accelerator. We investigate the importance of the joint exploration of DNN and edge GPU configurations. We propose an evolutionary-based co-optimization strategy for DNN by considering three metrics: DNN accuracy, execution latency, and power consumption. By combining the two search spaces, we have observed that we can explore more solutions and obtain a better tradeoff between DNN accuracy and hardware efficiency. Experimental results show that the co-optimization outperforms the optimization of DNN for fixed hardware configuration with up to 53% hardware efficiency gains for the same accuracy and latency.