wafa_ath
DIIDevHeads IoT Integration Server
•Created by wafa_ath on 8/30/2024 in #middleware-and-os
Tips for Simplifying ML Models to Avoid Inference Timeout on Arduino Nano 33 BLE Sense
Hi everyone,
Following up on my previous post regarding the "Inference Timeout on Layer 5" error in my vibration anomaly detection project on the Arduino Nano 33 BLE Sense, I’m looking for advice on reducing the model's complexity to avoid these timeouts.
The model was trained using Edge Impulse and includes multiple layers, with Layer 5 being particularly resource-intensive.
The Arduino Nano 33 BLE Sense seems to struggle with the inference at this layer, leading to timeouts.
I have some qst ..
What are the most effective strategies for simplifying a model without significantly compromising accuracy? Should I focus on reducing the number of layers, pruning parameters, or something else?
Are there any specific types of layers or operations that tend to be more efficient on microcontrollers like the Nano 33 BLE Sense?
I’m also open to exploring alternative approaches or model architectures that are better suited for real-time anomaly detection on low-power devices.
Thanks again for the help!
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