INSTRUCTIONS AND LOGIC TO PERFORM FLOATING POINT AND INTEGER OPERATIONS FOR MACHINE LEARNING

This abstract first appeared for US patent application 20240184572 titled 'INSTRUCTIONS AND LOGIC TO PERFORM FLOATING POINT AND INTEGER OPERATIONS FOR MACHINE LEARNING

Simplified Explanation

The abstract describes a graphics processing unit that accelerates machine-learning operations by performing a two-dimensional matrix multiply and accumulate operation.

Graphics processing unit with a single instruction, multiple thread (SIMT) architecture
Includes a multiprocessor that executes at least one single instruction
Contains a first compute unit within the multiprocessor to perform the matrix operation
The operation involves computing an intermediate product of 16-bit operands and a 32-bit sum based on the product

Potential Applications

This technology can be applied in various fields such as artificial intelligence, image and video processing, scientific computing, and data analytics.

Problems Solved

This technology solves the problem of accelerating machine-learning operations and improving the efficiency of two-dimensional matrix operations.

Benefits

The benefits of this technology include faster processing speeds, increased performance in machine-learning tasks, and enhanced capabilities for complex mathematical computations.

Potential Commercial Applications

Potential commercial applications of this technology include in data centers, cloud computing services, autonomous vehicles, robotics, and healthcare for medical imaging analysis.

Possible Prior Art

One possible prior art could be the use of general-purpose graphics processing units (GPGPUs) for accelerating machine-learning operations. These GPGPUs have been used in various applications requiring parallel processing capabilities.

What are the energy efficiency implications of using this technology?

Using this technology can lead to improved energy efficiency due to the accelerated processing speeds, which can reduce the overall power consumption of computing systems.

How does this technology compare to other hardware accelerators for machine learning?

This technology offers a specialized approach to accelerating machine-learning operations specifically tailored for two-dimensional matrix operations, providing a unique advantage over general-purpose hardware accelerators.

Original Abstract Submitted

one embodiment provides for a graphics processing unit to accelerate machine-learning operations, the graphics processing unit comprising a multiprocessor having a single instruction, multiple thread (simt) architecture, the multiprocessor to execute at least one single instruction; and a first compute unit included within the multiprocessor, the at least one single instruction to cause the first compute unit to perform a two-dimensional matrix multiply and accumulate operation, wherein to perform the two-dimensional matrix multiply and accumulate operation includes to compute an intermediate product of 16-bit operands and to compute a 32-bit sum based on the intermediate product.

Intel corporation (20240184572). INSTRUCTIONS AND LOGIC TO PERFORM FLOATING POINT AND INTEGER OPERATIONS FOR MACHINE LEARNING simplified abstract

Contents

INSTRUCTIONS AND LOGIC TO PERFORM FLOATING POINT AND INTEGER OPERATIONS FOR MACHINE LEARNING

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