# ECC: Platform-Independent Energy-Constrained Deep Neural Network Compression via a Bilinear Regression Model

arXiv: https://arxiv.org/abs/1812.01803 · HTML: https://mlsanity.com/paper/1812.01803/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2018.

## Built on by (most cited first, 15 of 15)

- [A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations](https://mlsanity.com/paper/2308.06767/) (2023)
- [Dual-side Sparse Tensor Core](https://mlsanity.com/paper/2105.09564/) (2021)
- [GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization](https://mlsanity.com/paper/2109.02220/) (2021)
- [Fire Together Wire Together: A Dynamic Pruning Approach with Self-Supervised Mask Prediction](https://mlsanity.com/paper/2110.08232/) (2021)
- [A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization](https://mlsanity.com/paper/2207.05650/) (2022)
- [FemtoDet: An Object Detection Baseline for Energy Versus Performance Tradeoffs](https://mlsanity.com/paper/2301.06719/) (2023)
- [Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks](https://mlsanity.com/paper/2308.04672/) (2023)
- [NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes](https://mlsanity.com/paper/2306.16869/) (2023)
- [A Closer Look at Data Augmentation Strategies for Finetuning-Based Low/Few-Shot Object Detection](https://mlsanity.com/paper/2408.10940/) (2024)
- [Hardware-Aware DNN Compression for Homogeneous Edge Devices](https://mlsanity.com/paper/2501.15240/) (2025)
- [MaskPrune: Mask-based LLM Pruning for Layer-wise Uniform Structures](https://mlsanity.com/paper/2502.14008/) (2025)
- [Architecture Aware Latency Constrained Sparse Neural Networks](https://mlsanity.com/paper/2109.00170/) (2021)
- [Joint Channel and Weight Pruning for Model Acceleration on Moblie Devices](https://mlsanity.com/paper/2110.08013/) (2021)
- [Learning Robust and Lightweight Model through Separable Structured Transformations](https://mlsanity.com/paper/2112.13551/) (2021)
- [MoSSP: A Momentum-Based Single-Loop Stochastic Penalty Method for Nonconvex Constrained DC-Regularized Optimization](https://mlsanity.com/paper/2605.29635/) (2026)

## Abstract (arXiv)

Many DNN-enabled vision applications constantly operate under severe energy constraints such as unmanned aerial vehicles, Augmented Reality headsets, and smartphones. Designing DNNs that can meet a stringent energy budget is becoming increasingly important. This paper proposes ECC, a framework that compresses DNNs to meet a given energy constraint while minimizing accuracy loss. The key idea of ECC is to model the DNN energy consumption via a novel bilinear regression function. The energy estimate model allows us to formulate DNN compression as a constrained optimization that minimizes the DNN loss function over the energy constraint. The optimization problem, however, has nontrivial constraints. Therefore, existing deep learning solvers do not apply directly. We propose an optimization algorithm that combines the essence of the Alternating Direction Method of Multipliers (ADMM) framework with gradient-based learning algorithms. The algorithm decomposes the original constrained optimization into several subproblems that are solved iteratively and efficiently. ECC is also portable across different hardware platforms without requiring hardware knowledge. Experiments show that ECC achieves higher accuracy under the same or lower energy budget compared to state-of-the-art resource-constrained DNN compression techniques.