Intel corporation (20240256845). MACHINE LEARNING SPARSE COMPUTATION MECHANISM FOR ARBITRARY NEURAL NETWORKS, ARITHMETIC COMPUTE MICROARCHITECTURE, AND SPARSITY FOR TRAINING MECHANISM simplified abstract

MACHINE LEARNING SPARSE COMPUTATION MECHANISM FOR ARBITRARY NEURAL NETWORKS, ARITHMETIC COMPUTE MICROARCHITECTURE, AND SPARSITY FOR TRAINING MECHANISM

Organization Name

intel corporation

Inventor(s)

Eriko Nurvitadhi of Hillsboro OR (US)

Amit Bleiweiss of Yad Binyamin (IL)

Deborah Marr of Portland OR (US)

Eugene Wang of Pittsburgh PA (US)

Saritha Dwarakapuram of Sunnyvale CA (US)

Sabareesh Ganapathy of Santa Clara CA (US)

MACHINE LEARNING SPARSE COMPUTATION MECHANISM FOR ARBITRARY NEURAL NETWORKS, ARITHMETIC COMPUTE MICROARCHITECTURE, AND SPARSITY FOR TRAINING MECHANISM - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240256845 titled 'MACHINE LEARNING SPARSE COMPUTATION MECHANISM FOR ARBITRARY NEURAL NETWORKS, ARITHMETIC COMPUTE MICROARCHITECTURE, AND SPARSITY FOR TRAINING MECHANISM

Simplified Explanation: The patent application describes an apparatus designed to process sparse matrices for arbitrary graph data efficiently. It includes a graphics processing unit with a data management unit that manages matrix operations, tracks active and skip logic for input operands, and processing circuitry with customizable functions.

• Key Features and Innovation:

   - Graphics processing unit with a data management unit for efficient matrix operations.
   - Active and skip logic for tracking input operands.
   - Customizable processing circuitry for custom functions.
   - Plurality of processing elements for reading operands and performing multiplication operations.

• Potential Applications:

   - Data processing for arbitrary graph data.
   - Optimization of sparse matrix operations.
   - Customizable functions for specific data processing needs.

• Problems Solved:

   - Efficient processing of sparse matrices.
   - Tracking and managing input operands effectively.
   - Customizable functions for specialized data processing requirements.

• Benefits:

   - Improved efficiency in processing arbitrary graph data.
   - Customizable functions for tailored data processing.
   - Enhanced performance in handling sparse matrices.

• Commercial Applications:

   - Title: "Efficient Sparse Matrix Processing Apparatus for Arbitrary Graph Data"
   - Potential commercial uses in data analytics, machine learning, and graph algorithms.
   - Market implications include improved performance and efficiency in data processing tasks.

• Prior Art:

   - Further research can be conducted in the field of graphics processing units, data management units, and customizable processing circuitry for data processing applications.

• Frequently Updated Research:

   - Stay updated on advancements in graphics processing units, matrix operations, and data processing algorithms relevant to this technology.

Questions about Sparse Matrix Processing Apparatus for Arbitrary Graph Data: 1. What are the key components of the apparatus described in the patent application?

  - The key components include a graphics processing unit with a data management unit, active and skip logic for input operands, and customizable processing circuitry.

2. How does the apparatus improve the processing of sparse matrices for arbitrary graph data?

  - The apparatus enhances efficiency by tracking active and skip logic for input operands, providing customizable functions, and utilizing a plurality of processing elements for matrix operations.

Original Abstract Submitted

an apparatus to facilitate processing of a sparse matrix for arbitrary graph data is disclosed. the apparatus includes a graphics processing unit having a data management unit (dmu) that includes a scheduler for scheduling matrix operations, an active logic for tracking active input operands, and a skip logic for tracking unimportant input operands to be skipped by the scheduler. processing circuitry is coupled to the dmu. the processing circuitry comprises a plurality of processing elements including logic to read operands and a multiplication unit to multiply two or more operands for the arbitrary graph data and customizable circuitry to provide custom functions.