Quantising event-camera networks to run under 1MB on a Cortex-M7

1. 1Build a gesture-recognition model for an event camera
2. 2Quantize the model using techniques such as integer quantization or knowledge distillation
3. 3Optimize the model for the Cortex-M7 architecture
4. 4Test the quantized model on the target device
5. 5Compare the performance of the quantized model with the original model

Embedded systems engineers and machine learning engineers can benefit from this technique to deploy models on resource-constrained devices

💡 Quantization can significantly reduce the memory footprint of machine learning models, enabling deployment on resource-constrained devices

🤖 Quantize event-camera networks to run under 1MB on a Cortex-M7! 💡