Leland Stanford Junior University

Ntt Research, Inc.

Daniel Kunin

Hidenori Tanaka

Daniel L K Yamins

Surya Ganguli

A system and method to prune parameters of a neural network at initialization uses iterative conserving synaptic flow that saves time, memory and energy both during training and at test time of the neural network. The result of the disclosed pruning system and method are highly sparse trainable subnetworks at initialization, without training and without ever looking at the data (a data agnostic pruning system and method). The pruning system and method preserves the total flow of synaptic strengths through the network at initialization subject to a sparsity constraint.

Growing community of inventors

Stanford, CA, United States of America

Daniel Kunin