Intel corporation (20240112033). HARDWARE IP OPTIMIZED CONVOLUTIONAL NEURAL NETWORK simplified abstract

HARDWARE IP OPTIMIZED CONVOLUTIONAL NEURAL NETWORK

Organization Name

intel corporation

Inventor(s)

Amit Bleiweiss of Yad Binyamin (IL)

Itamar Ben-ari of Givat HaShlosha (IL)

Michael Behar of Zichron Yaakov (IL)

Guy Jacob of Netanya (IL)

Gal Leibovich of Kiryat Yam (IL)

Jacob Subag of Kiryat Haim (IL)

Lev Faivishevsky of Kfar Saba (IL)

Yaniv Fais of Tel Aviv (IL)

Tomer Schwartz of Even Yehuda (IL)

HARDWARE IP OPTIMIZED CONVOLUTIONAL NEURAL NETWORK - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240112033 titled 'HARDWARE IP OPTIMIZED CONVOLUTIONAL NEURAL NETWORK

Simplified Explanation

The abstract describes an apparatus that includes an execution platform and logic to convert a trained neural network model into an optimized model for the execution platform.

• The apparatus comprises at least one execution platform.
• The logic, which includes hardware logic, receives a trained neural network model in a model optimizer and converts it to an optimized model suitable for the execution platform.

Potential Applications

The technology described in the patent application could be applied in various fields such as:

• Artificial intelligence
• Machine learning
• Neural network optimization

Problems Solved

The technology addresses the following problems:

• Efficient conversion of trained neural network models for specific execution platforms
• Optimization of neural network parameters for better performance

Benefits

The benefits of this technology include:

• Improved performance of neural network models on specific platforms
• Faster deployment of trained models in production environments

Potential Commercial Applications

The technology could have commercial applications in:

• Cloud computing services
• Edge computing devices
• AI-powered applications

Possible Prior Art

One possible prior art for this technology could be the development of model optimizers for neural networks on specific hardware platforms.

=== What are the specific hardware platforms supported by the logic in the apparatus? The abstract does not specify the specific hardware platforms supported by the logic in the apparatus.

=== How does the conversion process of the trained neural network model to an optimized model take place? The abstract does not provide details on the conversion process of the trained neural network model to an optimized model.

Original Abstract Submitted

in an example, an apparatus comprises at least one execution platform; and logic, at least partially including hardware logic, to receive a trained neural network model in a model optimizer and convert the trained neural network model to an optimized model comprising parameters that are fit to the at least one execution platform. other embodiments are also disclosed and claimed.