In today's digital era, image and video content dominate online traffic, accounting for the majority of global data transmission [1]. Efficient compression is therefore essential for delivering high-quality content under limited bandwidth and storage constraints. Recently, Deep Neural Networks (DNNs) have emerged as a powerful alternative to traditional compression methods, leveraging nonlinear representations to improve compression efficiency and visual quality [1].

However, beyond compression performance, practical deployment requires careful consideration of computational cost and energy consumption, especially on resource-constrained devices such as smartphones. Importantly, neither the number of parameters nor the number of operations in a DNN directly reflects its true energy consumption. Models with fewer parameters or operations may still consume more energy due to hardware-specific factors such as memory access patterns [2].

This project aims to systematically measure and model the runtime and energy consumption of DNNs across heterogeneous devices, including desktop GPUs, laptops, and/or smartphones. The objectives include refining and extending an existing energy measurement setup to ensure reliability and reproducibility, analyzing the relationship between model structure, runtime, and energy consumption, and designing predictive models for runtime and energy estimation across devices.

Existing approaches in the literature include device-specific [3] and device-adaptive [4] runtime estimation methods. Students are encouraged to explore and extend these approaches and to propose novel runtime and energy modeling techniques for the generalization across devices.

[1] JS Gomes, M. Grellert, FLL Ramos and S. Bampi, "End-to-End Neural Video Compression: A Review," IEEE Open Journal of Circuits and Systems, vol. 6, pp. 120-134, 2025.

[2] X. Yang, J. Kwon, Y. Li and Y. Chen, "Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning," CVPR, 2017.

[3] LL Zhang, S. Han, J. Wei, N. Zheng, T. Cao, Y. Yang and Y. Liu, "NN-Meter: Towards Accurate Latency Prediction of Deep-Learning Model Inference on Diverse Edge Devices," MobiSys, pp. 81-93, 2021.

[4] C. Feng, LL Zhang, Y. Liu, J. Xu, C. Zhang, Z. Wang, T. Cao, M. Yang and H. Tan, "LitePred: Transferable and Scalable Latency Prediction for Hardware-Aware Neural Architecture Search," NSDI, 2024.

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