Neural network operator fusion refers to combining multiple neural network operators (such as convolution, pooling, normalization, etc.) to improve computational efficiency and performance. The following are several common neural network operator fusion methods:
Kernel fusion: Merge multiple convolution kernels into a larger convolution kernel, thereby reducing the amount of calculation.
Layer Fusion: Combines multiple consecutive neural network layers into one larger layer, reducing memory access and computational overhead.
Data reuse: During the calculation process, multiple operators share input data to reduce the number of data reads.
Parallel computing: multiple independent neural network operators are computed in parallel to improve the overall computing speed.
Quantization Fusion: Convert floating-point calculations to fixed-point calculations to reduce calculation and storage overhead.
Group convolution: Divide the input and convolution kernel into multiple groups, and perform independent convolution operations on each group to reduce the amount of calculation.
Branch Fusion: The calculation results of different branches are fused to reduce the amount of calculation and memory access.
These methods can be selected and combined according to specific application scenarios and algorithm models to achieve higher computing efficiency and performance.