System and method for mathematical modeling of hardware quantization process

An apparatus, comprising:

• a processor;
• and
• a storage device coupled to the processor, wherein the storage device storing instructions which, when executed by the processor, cause the processor to perform a method for minimizing quantization accuracy losses when implementing a neural network node on a hardware device, the method comprising:
• identifying a set of adjustable parameters to be stored in the hardware device;
• modeling the quantization of neural network parameters and output of the neural network node on the hardware device as an optimization problem by formulating at least 9 an objective function, wherein the objective function is a function of the set of adjustable parameters;
• solving the optimization problem by identifying a set of values for the set of adjustable parameters that satisfies the objective function;
• and
• program the hardware device by configuring the set of adjustable parameters with the set of identified values, thereby allowing the programmed hardware device to implement the neural network node with improved precision at an output of the neural network node.

A non-transitory computer readable storage medium storing instructions which, when executed by a processor, causes the processor to perform a method for minimizing quantization accuracy losses when implementing a neural network node on a hardware device, the method comprising:

• one or more processors;
• and
• a memory coupled to the one or more processors, wherein the memory storing instructions that, when executed by the one or more processors, cause the edge device to:
• identifying a set of adjustable parameters to be stored in the hardware device;
• modeling the quantization of neural network parameters and output of the neural 5 network node on the hardware device as an optimization problem by formulating at least an objective function, wherein the objective function is a function of the set of adjustable parameters;
• solving the optimization problem by identifying a set of values for the set of adjustable parameters that satisfies the objective function;
• and
• program the hardware device by configuring the set of adjustable parameters with the set of identified values, thereby allowing the programmed hardware device to implement the neural network node with improved precision at an output of the neural network node.