Challenges in Mobile Deep Learning

As deep learning models grow larger and more complex, running them on mobile devices introduces unique challenges. From hardware limitations to software compatibility, there are hurdles to overcome.

I faced these challenges firsthand while deploying a neural network for facial recognition on Android. Optimizing it without compromising accuracy was a steep learning curve.

Some key challenges in mobile deep learning include:

• Model Size: Handling large models on limited storage.
• Inference Time: Reducing delays during predictions.
• Battery Consumption: Ensuring models run efficiently without draining power.