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 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.
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Bouzidi, H., Ouarnoughi, H., Talbi, EG., El Cadi, A.A., Niar, S. (2022). Evolutionary-Based Co-optimization of DNN and Hardware Configurations on Edge GPU. In: Dorronsoro, B., Pavone, M., Nakib, A., Talbi, EG. (eds) Optimization and Learning. OLA 2022. Communications in Computer and Information Science, vol 1684. Springer, Cham. https://doi.org/10.1007/978-3-031-22039-5_1
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