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Differentiable Neural Network Pruning to Enable Smart Applications on Microcontrollers

https://doi.org/10.1145/3569468
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2018.00171openalex-snapshot
MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00286openalex-snapshot
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-snapshot
MobileNetV2: Inverted Residuals and Linear Bottlenecks
inferredcites →https://doi.org/10.1109/cvpr.2019.00932openalex-snapshot
Structured Pruning of Neural Networks With Budget-Aware Regularization
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Estimating Movements of Human Body for the Shirt-Type Wearable Device Mounted on the Strain Sensors Based on Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/icassp.2018.8462688openalex-snapshot
Deep Residual Learning for Small-Footprint Keyword Spotting
inferredcites →https://doi.org/10.1109/icecs46596.2019.8964993openalex-snapshot
CoopNet: Cooperative Convolutional Neural Network for Low-Power MCUs
inferredcites →https://doi.org/10.1145/3131895openalex-snapshot
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EtinyNet: Extremely Tiny Network for TinyML

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Tiny Machine Learning for Enhanced Edge Intelligence
inferred← citeshttps://doi.org/10.1007/978-981-96-6941-7_11openalex-snapshot
Toward Efficient Deployment of Compressed Neural Networks on MCU for EdgeAI Applications
inferred← citeshttps://doi.org/10.1007/s10044-026-01696-xopenalex-snapshot
Efficient strawberry leaf classification with novel and ultra-lightweight Ghost-DeepResNet models
inferred← citeshttps://doi.org/10.1007/s11042-023-16740-9openalex-snapshot
TinyML: Tools, applications, challenges, and future research directions
inferred← citeshttps://doi.org/10.1016/j.ins.2024.120984openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.sna.2026.117502openalex-snapshot
Artificial intelligence-enabled MOS gas sensors: Towards Selective and intelligent detection in complex environments
inferred← citeshttps://doi.org/10.1021/acsnano.4c06144openalex-snapshot
Physical Reservoir Computing Using van der Waals Ferroelectrics for Acoustic Keyword Spotting
inferred← citeshttps://doi.org/10.1038/s41598-025-90764-zopenalex-snapshot
MicroNAS for memory and latency constrained hardware aware neural architecture search in time series classification on microcontrollers
inferred← citeshttps://doi.org/10.1038/s41598-026-44728-6openalex-snapshot
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inferred← citeshttps://doi.org/10.1108/jices-05-2025-0106openalex-snapshot
From Siri to Bixby: navigating the landscape of virtual voice assistants and their role in learning – a hybrid review
inferred← citeshttps://doi.org/10.1109/apccas62602.2024.10808973openalex-snapshot
Memory-Oriented Structural Pruning for Anomalous Sound Detection System on Microcontrollers
inferred← citeshttps://doi.org/10.1109/icncc68912.2025.00034openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icps65515.2025.11087914openalex-snapshot
Cascaded TinyML-Based Reduction for the Anomaly Detection Model of an Industrial Combustion System
inferred← citeshttps://doi.org/10.1109/imc-ssgp67001.2025.11474149openalex-snapshot
Embedded AI: Integrating TinyML in Resource-Constrained IoT Devices
inferred← citeshttps://doi.org/10.1109/jsen.2025.3544861openalex-snapshot
TinyFL_HKD: Enhancing Edge AI Federated Learning With Hierarchical Knowledge Distillation Framework
inferred← citeshttps://doi.org/10.1109/les.2025.3571799openalex-snapshot
Compressing Runtime Memory Usage via Activation Remapping for Deploying Deep Neural Networks on MCUs
inferred← citeshttps://doi.org/10.1109/newcas57931.2023.10198137openalex-snapshot
Iterative pruning algorithm for efficient look-up table implementation of binary neural networks
inferred← citeshttps://doi.org/10.1109/percom59722.2024.10494422openalex-snapshot
DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers
inferred← citeshttps://doi.org/10.1109/tmc.2025.3561778openalex-snapshot
Toward Diverse Tiny-Model Selection for Microcontrollers
inferred← citeshttps://doi.org/10.1109/tmc.2025.3564314openalex-snapshot
Synergy: Towards On-Body AI via Tiny AI Accelerator Collaboration on Wearables
inferred← citeshttps://doi.org/10.1109/tmc.2025.3594214openalex-snapshot
REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints
inferred← citeshttps://doi.org/10.1109/tmc.2026.3712264openalex-snapshot
Systematic Pruning and Acceleration for TinyML on Extremely Weak IoT Devices
inferred← citeshttps://doi.org/10.1109/tpami.2024.3355495openalex-snapshot
AIfES: A Next-Generation Edge AI Framework
inferred← citeshttps://doi.org/10.1145/3581783.3612412openalex-snapshot
ScaleFlow: Efficient Deep Vision Pipeline with Closed-Loop Scale-Adaptive Inference
inferred← citeshttps://doi.org/10.1145/3613904.3642109openalex-snapshot
Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences
inferred← citeshttps://doi.org/10.1145/3649476.3658699openalex-snapshot
Resource-Aware Saliency-Guided Differentiable Pruning for Deep Neural Networks
inferred← citeshttps://doi.org/10.3390/make8050112openalex-snapshot
Scaling Laws in the Tiny Regime: How Small Models Change Their Mistakes
inferred← citeshttps://doi.org/10.3390/s24237478openalex-snapshot
An Efficient Anomalous Sound Detection System for Microcontrollers
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