Intel corporation (20240126544). INSTRUCTIONS FOR FUSED MULTIPLY-ADD OPERATIONS WITH VARIABLE PRECISION INPUT OPERANDS simplified abstract

INSTRUCTIONS FOR FUSED MULTIPLY-ADD OPERATIONS WITH VARIABLE PRECISION INPUT OPERANDS

Organization Name

intel corporation

Inventor(s)

Dipankar Das of Pune (IN)

Naveen K. Mellempudi of Bangalore (IN)

Mrinmay Dutta of Bangalore (IN)

Arun Kumar of Bangalore (IN)

Dheevatsa Mudigere of Bangalore (IN)

Abhisek Kundu of Bangalore (IN)

INSTRUCTIONS FOR FUSED MULTIPLY-ADD OPERATIONS WITH VARIABLE PRECISION INPUT OPERANDS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240126544 titled 'INSTRUCTIONS FOR FUSED MULTIPLY-ADD OPERATIONS WITH VARIABLE PRECISION INPUT OPERANDS

Simplified Explanation

The disclosed embodiments relate to instructions for fused multiply-add (FMA) operations with variable-precision inputs. In one example, a processor executes an asymmetric FMA instruction by processing elements of the second source vector in SIMD lanes, multiplying each element by a corresponding element of the first source vector, and accumulating the results with previous contents of the destination.

• Processor executes asymmetric FMA instruction
• Fetch circuitry fetches FMA instruction with opcode, destination, and source vectors
• Decode circuitry decodes fetched FMA instruction
• SIMD execution circuit processes elements of second source vector in SIMD lanes
• Multiply each element by corresponding element of first source vector
• Accumulate results with previous contents of destination
• SIMD lane width can be 16 bits, 32 bits, or 64 bits
• First width can be 4 bits or 8 bits
• Second width can be 1 bit, 2 bits, or 4 bits

Potential Applications

This technology can be applied in:

• High-performance computing
• Scientific simulations
• Machine learning algorithms

Problems Solved

This technology solves:

• Efficient execution of FMA operations with variable-precision inputs
• Optimized processing of SIMD instructions
• Improved performance in vector processing tasks

Benefits

The benefits of this technology include:

• Increased computational efficiency
• Enhanced performance in SIMD operations
• Flexibility in handling variable-precision inputs

Potential Commercial Applications

The potential commercial applications of this technology include:

• Data centers
• Supercomputing facilities
• AI and machine learning platforms

Possible Prior Art

One possible prior art in this field is the use of SIMD instructions in processors for parallel processing tasks. Another prior art could be the implementation of FMA operations in high-performance computing systems.

How does this technology compare to traditional FMA operations in terms of performance and efficiency?

This article does not provide a direct comparison between this technology and traditional FMA operations.

Are there any limitations or constraints in implementing this technology in existing processor architectures?

This article does not address any limitations or constraints in implementing this technology in existing processor architectures.

Original Abstract Submitted

disclosed embodiments relate to instructions for fused multiply-add (fma) operations with variable-precision inputs. in one example, a processor to execute an asymmetric fma instruction includes fetch circuitry to fetch an fma instruction having fields to specify an opcode, a destination, and first and second source vectors having first and second widths, respectively, decode circuitry to decode the fetched fma instruction, and a single instruction multiple data (simd) execution circuit to process as many elements of the second source vector as fit into an simd lane width by multiplying each element by a corresponding element of the first source vector, and accumulating a resulting product with previous contents of the destination, wherein the simd lane width is one of 16 bits, 32 bits, and 64 bits, the first width is one of 4 bits and 8 bits, and the second width is one of 1 bit, 2 bits, and 4 bits.