Patent ID: 11922302
Assignee: KOREA ELECTRONICS TECHNOLOGY INSTITUTE
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 6:
7. A computer-implemented method of optimizing hyperparameters through statistical analysis in a spiking neural network (SNN) system comprising an SNN accelerator and a hyperparameter optimizer connected to the SNN accelerator, the SNN accelerator comprising an neuromorphic chip and configured to perform learning and make an inference, the hyperparameters comprising two or more of a length of spike train, an amount of neurons in a spiking neural network, a generation frequency of input spikes, a synapse weight learning rate, a threshold learning rate, an initial threshold value, or a membrane time constant, the method comprising:
receiving hardware performance elements from the SNN accelerator connected to the hyperparameter optimizer, the hardware performance elements being indicative of hardware constraints of the SNN accelerator;
defining hyperparameter-specific allowable ranges for the hyperparameters based on the hardware performance elements;
receiving training data comprising pieces of data;
processing the training data and computing a statistical value including at least one of an amount of the pieces of data, label-specific distribution characteristics, a minimum data value, a maximum data value, a variance, or a standard deviation regarding the training data;
generating hyperparameter-specific objective functions for the hyperparameters based on the computed statistical value;
performing regression on the hyperparameter-specific objective functions;
selecting hyperparameters using the hyperparameter-specific objective functions according to certain selection rules and further based on the hyperparameter-specific allowable ranges; and
transferring the selected hyperparameters to the SNN accelerator so that the neuromorphic chip is configured to perform on-chip learning using the selected hyperparameters.