Answer / Saurabh Saraswat

To implement AI on a low-power device with limited memory, several techniques can be considered:
1. Quantization: Reduce the number of bits used to represent weights in neural networks, thereby reducing storage requirements and power consumption.
2. Pruning: Remove redundant connections in neural networks to reduce their size and computational complexity.
3. Model compression: Apply techniques like knowledge distillation or neural architecture search to create smaller yet efficient models.
4. Low-rank approximations: Approximate high-dimensional matrices with lower-rank alternatives, reducing memory requirements.

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