View source for 18589092. COMPRESSING A NEURAL NETWORK simplified abstract (Imagination Technologies Limited)

=COMPRESSING A NEURAL NETWORK=


==Organization Name==

[[:Category:Imagination Technologies Limited|Imagination Technologies Limited]]


[[Category:Imagination Technologies Limited]]

==Inventor(s)==

[[:Category:Gunduz Vehbi Demirci of Hertfordshire (GB)|Gunduz Vehbi Demirci of Hertfordshire (GB)]][[Category:Gunduz Vehbi Demirci of Hertfordshire (GB)]]

[[:Category:Cagatay Dikici of Hertfordshire (GB)|Cagatay Dikici of Hertfordshire (GB)]][[Category:Cagatay Dikici of Hertfordshire (GB)]]

==COMPRESSING A NEURAL NETWORK - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18589092 titled 'COMPRESSING A NEURAL NETWORK

The abstract describes a method for compressing a neural network by rearranging the elements of a matrix representing a layer of the network to create sub-matrices with a higher density of non-zero values.

* The method involves receiving a neural network and determining a matrix of coefficients for a layer.
* The matrix is rearranged to group non-zero values into sub-matrices with higher density.
* The compressed neural network includes a compressed layer that performs operations based on the sub-matrices.

Potential Applications:
- Efficient storage and transmission of neural networks.
- Faster inference and reduced computational requirements.
- Improved performance in resource-constrained environments.

Problems Solved:
- Addressing the challenge of large neural network sizes.
- Enhancing the scalability and practicality of neural network deployment.
- Optimizing neural network operations for better efficiency.

Benefits:
- Reduced memory and processing requirements.
- Faster execution of neural network tasks.
- Enhanced performance in edge computing and IoT devices.

Commercial Applications:
Title: "Efficient Neural Network Compression for Enhanced Performance"
This technology can be applied in industries such as:
- Mobile devices for faster AI applications.
- Cloud computing for optimized resource utilization.
- Autonomous vehicles for real-time decision-making.

Questions about Neural Network Compression:
1. How does rearranging the matrix elements improve neural network compression?
2. What are the implications of using sub-matrices for compressed neural network operations?

Frequently Updated Research:
Stay updated on advancements in neural network compression techniques and their impact on AI applications.


==Original Abstract Submitted==

A computer implemented method of compressing a neural network, the method comprising: receiving a neural network; determining a matrix representative of a set of coefficients of a layer of the received neural network, the layer being arranged to perform an operation, the matrix comprising a plurality of elements representative of non-zero values and a plurality of elements representative of zero values; rearranging the rows and/or columns of the matrix so as to gather the plurality of elements representative of non-zero values of the matrix into one or more sub-matrices, the one or more sub-matrices having a greater number of elements representative of non-zero values per total number of elements of the one or more sub-matrices than the number of elements representative of non-zero values per total number of elements of the matrix; and outputting a compressed neural network comprising a compressed layer arranged to perform a compressed operation in dependence on the one or more sub-matrices.



[[Category:G06N3/0495]]
[[Category:CPC_G06N3/0495]]