When Smaller Wins: Dual-Stage Distillation and Pareto-Guided Compression of Liquid Neural Networks for Edge Battery Prognostics
arXiv:2601.06227v3 Announce Type: replace-cross Abstract: Battery management systems increasingly require accurate battery health prognostics under strict on-device constraints. This paper presents DLNet, a practical framework with dual-stage distillation of liquid neural networks that turns a high-capacity model into compact and edge-deployable models for battery health prediction. DLNet first applies Euler discretization to reformulate liquid dynamics for embedded compatibility. It then performs dual-stage knowledge distillation to transfer the teacher model's temporal behavior and recover i
The increasing demand for on-device AI applications and the constraints of edge computing require innovative solutions for deploying complex models efficiently.
This development allows for the deployment of advanced AI prognostics on resource-limited edge devices, improving battery management and potentially extending device lifespan without cloud dependency.
A framework for practical implementation of high-capacity liquid neural networks on edge devices through distillation and compression is now available, broadening the scope of real-time, on-device AI applications.
- · Edge AI hardware manufacturers
- · Battery management systems developers
- · IoT device industry
- · AI model compression techniques
- · Cloud-dependent prognostics services
- · Hardware-intensive AI models for edge
Improved battery life and reliability for a wide range of devices due to accurate on-device prognostics.
Accelerated adoption of more complex AI models in constrained edge environments across various industries.
Reduced overall energy consumption and e-waste due to optimized device usage and extended product lifecycles.
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Read at arXiv cs.AI