Neural Network Calculation Device Pre-scaling Weight Matrix

The patent applies parameter changes by converting between floating-point and integer representations dynamically. The system maintains floating-point precision for accurate calculations during matrix operations, then converts to integer format for faster subsequent computations. This parameter transformation allows the system to leverage the high accuracy of floating-point arithmetic when needed while achieving the speed benefits of integer operations, thereby resolving the contradiction between accuracy and computing performance.

The patent implements preliminary action by performing floating-point matrix operations and conversions in advance before integer operations are executed. The system pre-computes scaled weight matrices and performs necessary numerical conversions beforehand, so that when integer operations are performed, the data is already in the appropriate format. This preliminary preparation allows the system to achieve high computing performance through integer operations while maintaining the accuracy benefits of floating-point arithmetic through the pre-computed scaled representations.

The patent applies preliminary action by performing numerical conversions and scaling operations in advance during model training or initialization, rather than during each forward propagation step. The system pre-scales weight matrices and performs floating-point-to-integer conversions beforehand, storing the converted values for use during inference. This approach maintains the flexibility to switch between floating-point and integer operations while significantly reducing computational time by eliminating repeated conversions during actual neural network computations.

The patent implements preliminary action by performing scaling operations during model training or initialization rather than during each numerical conversion. The system pre-computes scaled weight matrices and stores them, eliminating the need for repeated scaling during inference. This approach maintains conversion accuracy by using pre-scaled representations while significantly reducing device complexity by removing the need for dynamic scaling circuitry during forward propagation, thereby resolving the contradiction between conversion accuracy and device complexity.