Neural Architectures TRIZ Cases
- Dynamic Quantization Precision for Deep Learning Networks
Deep learning networks select quantization models based on input uncertainty to maximize computation efficiency while reducing power consumption.
- Sparse Memory Access for Neural Network Accuracy
Selective weight generation reduces computational complexity and memory overhead while maintaining high processing accuracy.