# Trading-off Accuracy and Energy of Deep Inference on Embedded Systems: A Co-Design Approach

arXiv: https://arxiv.org/abs/1901.10584 · HTML: https://mlsanity.com/paper/1901.10584/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2019.

## Built on by (most cited first, 8 of 8)

- [DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device](https://mlsanity.com/paper/2104.09949/) (2021)
- [Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design](https://mlsanity.com/paper/2103.15750/) (2021)
- [Multi-Objective Optimization of ReRAM Crossbars for Robust DNN Inferencing under Stochastic Noise](https://mlsanity.com/paper/2109.05437/) (2021)
- [Multimodal Adaptive Inference for Document Image Classification with Anytime Early Exiting](https://mlsanity.com/paper/2405.12705/) (2024)
- [Reducing Inference Energy Consumption Using Dual Complementary CNNs](https://mlsanity.com/paper/2412.01039/) (2024)
- [SETGAN: Scale and Energy Trade-off GANs for Image Applications on Mobile Platforms](https://mlsanity.com/paper/2103.12896/) (2021)
- [A Converting Autoencoder Toward Low-latency and Energy-efficient DNN Inference at the Edge](https://mlsanity.com/paper/2403.07036/) (2024)
- [Efficient Single Object Detection on Image Patches with Early Exit Enhanced High-Precision CNNs](https://mlsanity.com/paper/2309.03530/) (2023)

## Abstract (arXiv)

Deep neural networks have seen tremendous success for different modalities of data including images, videos, and speech. This success has led to their deployment in mobile and embedded systems for real-time applications. However, making repeated inferences using deep networks on embedded systems poses significant challenges due to constrained resources (e.g., energy and computing power). To address these challenges, we develop a principled co-design approach. Building on prior work, we develop a formalism referred to as Coarse-to-Fine Networks (C2F Nets) that allow us to employ classifiers of varying complexity to make predictions. We propose a principled optimization algorithm to automatically configure C2F Nets for a specified trade-off between accuracy and energy consumption for inference. The key idea is to select a classifier on-the-fly whose complexity is proportional to the hardness of the input example: simple classifiers for easy inputs and complex classifiers for hard inputs. We perform comprehensive experimental evaluation using four different C2F Net architectures on multiple real-world image classification tasks. Our results show that optimized C2F Net can reduce the Energy Delay Product (EDP) by 27 to 60 percent with no loss in accuracy when compared to the baseline solution, where all predictions are made using the most complex classifier in C2F Net.