公开(公告)号：US20240078432A1

公开(公告)日：2024-03-07

申请号：US18508248

申请日：2023-11-14

Applicant: Kneron Inc.

Inventor： JIE WU ,  JUNJIE SU ,  BIKE XIE ,  Chun-Chen Liu

IPC: G06N3/082 ,  G06N3/04

CPC classification number: G06N3/082 ,  G06N3/04

Abstract: A self-tuning model compression methodology for reconfiguring a Deep Neural Network (DNN) includes: receiving a pre-trained DNN model and a data set; performing an inter-layer sparsity analysis to generate a first sparsity result; and performing an intra-layer sparsity analysis to generate a second sparsity result, including: defining a plurality of sparsity metrics for the network; performing forward and backward passes to collect data corresponding to the sparsity metrics; using the collected data to calculate values for the defined sparsity metrics; and visualizing the calculated values using at least a histogram. The methodology further includes: according to the first and second sparsity results, performing low-rank approximation on the pre-trained DNN; pruning the represented DNN model according to the first and second sparsity results; performing quantization on the pruned DNN model according to the first and second sparsity results; and executing the reconfigured model on a user terminal for an end-user application.