USING OUTPUT EQUALIZATION IN TRAINING AN ARTIFICIAL INTELLIGENCE MODEL IN A SEMICONDUCTOR SOLUTION

摘要

A system for training an artificial intelligence (AI) model for an AI chip may include an AI training unit to train weights of an AI model in floating point, and one or more quantization units for updating the weights of the AI model while accounting for the hardware constraints in the AI chip. The system may also include customization unit for performing one or more linear transformations on the updated weights. The system may also perform output equalization for one or more convolution layers of the AI model to equalize the inputs and/or outputs of each layer of the AI model to within the range allowed in the physical AI chip. The system may further update the weights by performing shift-based quantization that mimics the characteristics of a hardware chip. The updated weights may be stored in fixed point and uploadable to an AI chip implementing an AI task.