Elastoformer: Enabling Dynamic Adaptivity via Elastic Model Transformation

2026-09-09

AI Summary

为解决边缘设备动态条件下DNN适应性差的问题，提出Elastoformer框架，通过弹性模型转换实现动态适应，提高效率。

Abstract

EdgeAI systems are increasingly employing computer vision applications to enable intelligent, on-device decision-making in real-time. However, these deployments face highly dynamic operational conditions, with fluctuating constraints on latency, power availability, and memory resources. Deep Neural Networks (DNN), which follow fixed computational execution flows, lack the flexibility to adapt to such variability, resulting in inefficient and suboptimal performance in edge scenarios. This underscores the need for architectures that are not only efficient but also dynamically scalable at runtime. In this paper, we propose Elastoformer: A framework that transforms conventional neural networks (NN) into Elastic NN capable of real-time elastic inference. Unlike the conventional bag-of-models approach, which requires maintaining multiple independent models for different operating conditions, Elastoformer offers a single, modular solution that dynamically switches between multiple modes of operation at runtime, adapting efficiently to the changing computational budgets of edge devices without the overhead of managing separate models. Experiments reveal that our framework achieves up to 85% reduction in computation FLOPs, 50% reduction in latency and 76% reduction in memory overhead, while showcasing the architecture agnostic nature of the framework across both Vision Transformers and CNNs. Our code is available at https://github.com/sudaksh14/Elastoformer.

Problem

• EdgeAI
• Dynamic Adaptivity
• Deep Neural Networks
• Operational Conditions
• Elastic Inference

Innovation

• Elastic Model Transformation
• Dynamic Adaptivity
• Real-time Elastic Inference
• EdgeAI