2. Quantization & Compression "3. AI Algorithm Optimization"

By reducing the bit precision of models, storage and computational demands can be decreased while maintaining the required accuracy. This process typically involves converting floating-point weights into lower-bit representations (such as 8-bit or 16-bit integers) to reduce the model size and accelerate computation speed.

Process: After the model training is complete, quantization is performed to reduce the parameter bit count. During this process, specific techniques are employed to adjust the model so that it can maintain high accuracy even after quantization. This often includes fine-tuning steps to ensure stable performance of the quantized model, minimizing accuracy loss.

Applications: Quantization and compression techniques are particularly suitable for edge devices and mobile devices, allowing complex deep learning tasks to be executed even in environments with limited computational power and storage space, thereby enhancing user experience.