US 2025/0053806 A1 · SPARSIFICATION TARGET LAYER DETERMINATION APPARATUS, SPARSIFICATION TARGET LAYER DETERMINATION METHOD, AND PROGRAM

Abstract

A sparsification target layer determination apparatus includes: an each-layer sparsity speed contribution investigation part which receives a neural network model which includes a plurality of layers each of which has weights and one or more sparse weight neural network models which have sparse weights obtained by applying sparsification to the weights, layer by layer, and investigates, layer by layer, an execution time of the neural network model and one or more execution times of the one or more sparse weight neural network models; and a sparsification target layer determination part which determines whether or not to apply sparsification to the weights of the neural network model, layer by layer, based on a result of the investigation.

Claims18

Dependent claims

A sparsification target layer determination apparatus, comprising:

at least a processor;
and
a memory in circuit communication with the processor,
wherein the processor is configured to execute program instructions stored in the memory to perform:
receiving a neural network model which includes a plurality of layers each of which has weights and one or more sparse weight neural network models which have sparse weights obtained by applying sparsification to the weights, layer by layer, and investigates investigating, layer by layer, an execution time of the neural network model and one or more execution times of the one or more sparse weight neural network models;
and
determining whether or not to apply sparsification to the weights of the neural network model, layer by layer, based on a result of the investigation.

4 more elements

A sparsification target layer determination method, comprising:

performed by a computer including a processor and a memory,
receiving a neural network model which includes a plurality of layers each of which has weights and one or more sparse weight neural network models which have sparse weights obtained by applying sparsification to the weights, layer by layer, and investigating, layer by layer, an execution time of the neural network model and one or more execution times of the one or more sparse weight neural network models;
and
determining whether or not to apply sparsification to the weights of the neural network model, layer by layer, based on a result of the investigation.

1 more element

A computer-readable non-transitory recording medium recording a program, the program causing a computer to perform processings of:

receiving a neural network model which includes a plurality of layers each of which has weights and one or more sparse weight neural network models which have sparse weights obtained by applying sparsification to the weights layer by layer and investigating, layer by layer, an execution time of the neural network model and one or more execution times of the one or more sparse weight neural network models;
and
determining whether or not to apply sparsification to the weights of the neural network model, layer by layer, based on a result of the investigation.

Dependencies are inferred from the claim text.

Sparsification target layer determination apparatus, sparsification target layer determination method, and program

Life of this patent

Sparsification target layer determination apparatus, sparsification target layer determination method, and program

Life of this patent

Abstract

Claims18