mlsanityData to 14 Oct 2026

Efficient Network Inference via Hardware-Aware Architecture Search, Model Pruning & Quantization

arXiv 2606.23210 ↗First posted 22 Jun 2026cs.LG cs.DC eess.SPcited by 0

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Abstract (arXiv)

Embedded global navigation satellite system (GNSS) interference monitoring requires fast and memory-efficient inference to process large volumes of raw in-phase and quadrature (IQ) samples in real time. At the same time, increasingly expressive deep neural networks (DNNs) are needed for robust interference classification and characterization across diverse signal conditions. This creates a fundamental tension between predictive performance and deployability on resource-constrained hardware. In this paper, we investigate efficient network inference for GNSS interference characterization using iterative structured pruning, post-training static quantization, and hardware-aware zero-shot neural architecture search (NAS). Starting from MCUNet as a compact baseline, we analyze how model compression and automated architecture optimization affect model size, computational complexity, and memory usage while maintaining task performance. Experiments on a GNSS interference dataset, covering both classification and generalized characterization, show the benefits of combining compression and hardware-aware design for embedded deployment. Our results provide practical guidance for developing compact machine learning (ML) models for real-time GNSS interference monitoring on embedded platforms (iMXRT1062 MCU, Raspberry Pi Zero 2W, and Raspberry Pi 5).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 34 references that are in the map. First posted on arXiv 22 Jun 2026.

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23 of 34 references.

References · 23

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  3. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
  4. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,593
  5. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,060
  6. 2018SNIP: Single-shot Network Pruning based on Connection Sensitivitycited by 812
  7. 2019Once-for-All: Train One Network and Specialize it for Efficient Deploymentcited by 787
  8. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  9. 2020MCUNet: Tiny Deep Learning on IoT Devicescited by 273
  10. 2020Neural Architecture Search without Trainingcited by 244
  11. 2021MLPerf Tiny Benchmarkcited by 124
  12. 2023ZiCo: Zero-shot NAS via Inverse Coefficient of Variation on Gradientscited by 58
11 more
  1. 2022Concept-level Debugging of Part-Prototype Networkscited by 52
  2. 2022Energy-efficient Deployment of Deep Learning Applications on Cortex-M based Microcontrollers using Deep Compressioncited by 7
  3. 2023Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyMLcited by 6
  4. 2026GAC-KAN: An Ultra-Lightweight GNSS Interference Classifier for GenAI-Powered Consumer Edge Devicescited by 5
  5. 2024Low-Energy On-Device Personalization for MCUscited by 3
  6. 2025EMind: A Foundation Model for Multi-task Electromagnetic Signals Understandingcited by 3
  7. 2024GNSS Interference Classification Using Federated Reservoir Computingcited by 1
  8. 2025VAE-based Feature Disentanglement for Data Augmentation and Compression in Generalized GNSS Interference Classificationcited by 1
  9. 2025Dictionary-Based Contrastive Learning for GNSS Jamming Detectioncited by 1
  10. 2026PrototypeNAS: Rapid Design of Deep Neural Networks for Microcontroller Unitscited by 1
  11. 2026GenAI for Energy-Efficient and Interference-Aware Compressed Sensing of GNSS Signals on a Google Edge TPUcited by 1

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