Intel corporation (20240126545). SYSTEMS AND METHODS FOR PERFORMING 16-BIT FLOATING-POINT MATRIX DOT PRODUCT INSTRUCTIONS simplified abstract

SYSTEMS AND METHODS FOR PERFORMING 16-BIT FLOATING-POINT MATRIX DOT PRODUCT INSTRUCTIONS

Organization Name

intel corporation

Inventor(s)

Alexander F. Heinecke of San Jose CA (US)

Robert Valentine of Kiryat Tivon (IL)

Mark J. Charney of Lexington MA (US)

Raanan Sade of Portland OR (US)

Menachem Adelman of Modi'in (IL)

Zeev Sperber of Zichron Yackov (IL)

Amit Gradstein of Binyamina (IL)

Simon Rubanovich of Haifa (IL)

SYSTEMS AND METHODS FOR PERFORMING 16-BIT FLOATING-POINT MATRIX DOT PRODUCT INSTRUCTIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240126545 titled 'SYSTEMS AND METHODS FOR PERFORMING 16-BIT FLOATING-POINT MATRIX DOT PRODUCT INSTRUCTIONS

Simplified Explanation

The patent application describes a processor that computes dot products of nibbles in tile operands, allowing for efficient matrix multiplication operations.

• Decode circuitry decodes a tile dot product instruction with fields for opcode, destination identifier, first source identifier, and second source identifier.
• Execution circuitry executes the instruction to generate products by multiplying corresponding nibbles of doubleword elements in the specified matrices and accumulating the results.

Potential Applications

This technology can be applied in various fields such as image processing, machine learning, and signal processing where matrix multiplication is a common operation.

Problems Solved

This technology solves the problem of efficiently computing dot products of nibbles in matrices, enabling faster and more efficient matrix multiplication operations.

Benefits

The benefits of this technology include improved performance in matrix multiplication tasks, reduced power consumption, and enhanced overall efficiency of computing systems.

Potential Commercial Applications

Potential commercial applications of this technology include in hardware accelerators for deep learning, scientific computing, and other matrix-intensive applications.

Possible Prior Art

One possible prior art for this technology could be existing methods for optimizing matrix multiplication operations in processors or specialized hardware accelerators.

Unanswered Questions

How does this technology compare to existing methods for matrix multiplication acceleration?

The article does not provide a direct comparison to existing methods for accelerating matrix multiplication operations. Further research or comparative analysis would be needed to evaluate the effectiveness of this technology in comparison to other approaches.

What are the limitations or constraints of implementing this technology in practical computing systems?

The article does not address the potential limitations or constraints of implementing this technology in real-world computing systems. Factors such as hardware compatibility, scalability, and cost implications would need to be considered for practical deployment.

Original Abstract Submitted

disclosed embodiments relate to computing dot products of nibbles in tile operands. in one example, a processor includes decode circuitry to decode a tile dot product instruction having fields for an opcode, a destination identifier to identify a m by n destination matrix, a first source identifier to identify a m by k first source matrix, and a second source identifier to identify a k by n second source matrix, each of the matrices containing doubleword elements, and execution circuitry to execute the decoded instruction to perform a flow k times for each element (m, n) of the specified destination matrix to generate eight products by multiplying each nibble of a doubleword element (m,k) of the specified first source matrix by a corresponding nibble of a doubleword element (k,n) of the specified second source matrix, and to accumulate and saturate the eight products with previous contents of the doubleword element.