When Smaller Wins: Dual-Stage Distillation and Pareto-Guided Compression of Liquid Neural Networks for Edge Battery Prognostics

2026-01-08 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Internet of Things (IoT) & Connected Devices, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

DLNet is a practical framework designed for deploying compact, accurate battery health prognostics models on edge devices. It employs dual-stage distillation and Pareto-guided compression of liquid neural networks (LNNs). The framework first reformulates LNN dynamics using Euler discretization for embedded compatibility, then applies dual-stage knowledge distillation to transfer the high-capacity teacher model's temporal behavior to smaller student models. Pareto-guided selection, based on joint error-cost objectives, retains student models that balance accuracy and efficiency. Validated on the MIT-Stanford battery dataset and deployed on an Arduino Nano 33 BLE Sense using int8 quantization, DLNet's final student model achieved a 0.0066 MAE for predicting battery health over 100 cycles, representing a 15.4% error reduction compared to the teacher. It also reduced model size from 616 kB to 94 kB (an 84.7% reduction) and performed inference in 21 ms on the device. This demonstrates that smaller models can surpass larger teachers for edge prognostics with proper supervision.

Key takeaway

For Machine Learning Engineers developing battery management systems, DLNet offers a proven strategy to deploy high-accuracy prognostics on constrained edge hardware. You should consider dual-stage distillation and Pareto-guided compression to achieve significant model size reductions, like 84.7% from 616 kB to 94 kB, while improving prediction accuracy by 15.4% MAE. This approach ensures your models are both performant and feasible for real-world embedded applications, such as on Arduino Nano 33 BLE Sense.

Key insights

Dual-stage distillation and Pareto-guided compression enable accurate, compact LNNs for edge battery prognostics.

Principles

• Euler discretization simplifies LNN dynamics for edge.
• Dual-stage distillation recovers post-compression performance.
• Pareto analysis optimizes error-cost trade-offs.

Method

DLNet trains a high-capacity LNN teacher, creates Euler-discretized student variants, applies dual-stage distillation with pruning, and uses Pareto selection for optimal error-cost balance before int8 quantization and deployment.

In practice

• Deploy LNNs for battery health on microcontrollers.
• Apply dual-stage distillation to other time-series models.
• Use Pareto optimization for resource-constrained ML.

Topics

• Liquid Neural Networks
• Knowledge Distillation
• Model Compression
• Edge AI
• Battery Prognostics
• Pareto Optimization

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Hardware Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.