# ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge

arXiv: https://arxiv.org/abs/2507.06011 · HTML: https://mlsanity.com/paper/2507.06011/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 16 of 16)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [TensorOpera Router: A Multi-Model Router for Efficient LLM Inference](https://mlsanity.com/paper/2408.12320/) (2024)
- [NoScope: Optimizing Neural Network Queries over Video at Scale](https://mlsanity.com/paper/1703.02529/) (2017)
- [Routoo: Learning to Route to Large Language Models Effectively](https://mlsanity.com/paper/2401.13979/) (2024)
- [Smoothie: Label Free Language Model Routing](https://mlsanity.com/paper/2412.04692/) (2024)
- [CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing](https://mlsanity.com/paper/2502.01976/) (2025)
- [PickLLM: Context-Aware RL-Assisted Large Language Model Routing](https://mlsanity.com/paper/2412.12170/) (2024)
- [SelectLLM: Query-Aware Efficient Selection Algorithm for Large Language Models](https://mlsanity.com/paper/2408.08545/) (2024)
- [Towards Self-Adaptive Machine Learning-Enabled Systems Through QoS-Aware Model Switching](https://mlsanity.com/paper/2308.09960/) (2023)
- [SWITCH: An Exemplar for Evaluating Self-Adaptive ML-Enabled Systems](https://mlsanity.com/paper/2402.06351/) (2024)
- [EdgeMLBalancer: A Self-Adaptive Approach for Dynamic Model Switching on Resource-Constrained Edge Devices](https://mlsanity.com/paper/2502.06493/) (2025)
- [Context-aware Multi-Model Object Detection for Diversely Heterogeneous Compute Systems](https://mlsanity.com/paper/2402.07415/) (2024)
- [An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge](https://mlsanity.com/paper/2309.00022/) (2023)

## Abstract (arXiv)

Edge computing enables data processing closer to the source, significantly reducing latency, an essential requirement for real-time vision-based analytics such as object detection in surveillance and smart city environments. However, these tasks place substantial demands on resource-constrained edge devices, making the joint optimization of energy consumption and detection accuracy critical. To address this challenge, we propose ECORE, a framework that integrates multiple dynamic routing strategies, including a novel estimation-based techniques and an innovative greedy selection algorithm, to direct image processing requests to the most suitable edge device-model pair. ECORE dynamically balances energy efficiency and detection performance based on object characteristics. We evaluate our framework through extensive experiments on real-world datasets, comparing against widely used baseline techniques. The evaluation leverages established object detection models (YOLO, SSD, EfficientDet) and diverse edge platforms, including Jetson Orin Nano, Raspberry Pi 4 and 5, and TPU accelerators. Results demonstrate that our proposed context-aware routing strategies can reduce energy consumption and latency by 35% and 49%, respectively, while incurring only a 2% loss in detection accuracy compared to accuracy-centric methods.