Neural Network Quantization Parameter Determination Method

The patent changes the precision parameter of data from high-precision floating-point format to low-precision fixed-point format through quantization. By adjusting the bit width parameter during quantization, the system transforms data representation while maintaining acceptable precision levels, thereby reducing storage space requirements.

The patent creates a quantized copy of the original high-precision data. This quantized version serves as an approximation that occupies less storage space while preserving the essential information needed for neural network operations, effectively replacing the need to store full-precision data.

The patent changes the data precision parameter to reduce power consumption. By using lower precision fixed-point arithmetic instead of high-precision floating-point operations, the energy required for computation is significantly reduced, making the system more energy-efficient.

The patent uses quantized data copies that require less computational resources to process. These lower-precision representations reduce the power consumption associated with data processing while maintaining sufficient accuracy for neural network tasks.

The patent employs dynamic quantization where the bit width and precision levels are adjusted based on the specific requirements of different neural network layers and operations. This dynamic adjustment allows the system to use lower precision where acceptable and maintain higher precision where needed, optimizing the trade-off between storage space and precision.

The patent applies different quantization precision levels to different parts of the neural network based on their specific requirements. Critical layers that require high precision maintain higher bit widths, while less critical layers use lower precision, creating a locally optimized precision distribution across the network.

The patent uses dynamic precision adjustment to balance energy consumption and precision requirements. By adaptively selecting appropriate precision levels for different computational tasks, the system minimizes energy consumption while ensuring that precision requirements are met for critical operations.

The patent applies differentiated precision strategies to different neural network components. Energy-intensive operations that can tolerate lower precision use reduced bit widths, while operations requiring high precision maintain higher precision levels, optimizing the energy-precision trade-off locally across the system.