Experimental benchmarks show ESP-NOW protocol yields lowest latency in TinyML split learning on ESP32-S3, with a beam search optimizer achieving near-optimal split points in 0.1 seconds for small device groups.
Split computing and early exiting for deep learning applications: Survey and research challenges,
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NaviSplit introduces a dynamic multi-branch split DNN framework for UAV navigation that runs perception on-device and control on-edge, achieving 72-81% depth accuracy with 1.2-18 KB transmissions and 95% lower data rate than static alternatives.
NaviSlim uses a gated slimmable architecture to dynamically scale neural model complexity and onboard sensor power for context-aware navigation in micro-drones, reporting 57-92% average model reduction and 61-80% sensor utilization in AirSim simulations versus static full-complexity baselines.
citing papers explorer
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Optimizing Split Learning Latency in TinyML-Based IoT Systems
Experimental benchmarks show ESP-NOW protocol yields lowest latency in TinyML split learning on ESP32-S3, with a beam search optimizer achieving near-optimal split points in 0.1 seconds for small device groups.
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NaviSplit: Dynamic Multi-Branch Split DNNs for Efficient Distributed Autonomous Navigation
NaviSplit introduces a dynamic multi-branch split DNN framework for UAV navigation that runs perception on-device and control on-edge, achieving 72-81% depth accuracy with 1.2-18 KB transmissions and 95% lower data rate than static alternatives.
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NaviSlim: Adaptive Context-Aware Navigation and Sensing via Dynamic Slimmable Networks
NaviSlim uses a gated slimmable architecture to dynamically scale neural model complexity and onboard sensor power for context-aware navigation in micro-drones, reporting 57-92% average model reduction and 61-80% sensor utilization in AirSim simulations versus static full-complexity baselines.