Intel corporation (20240160405). COMPUTATION OF CORRECTLY ROUNDED FLOATING POINT SUMMATION simplified abstract
Contents
- 1 COMPUTATION OF CORRECTLY ROUNDED FLOATING POINT SUMMATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 COMPUTATION OF CORRECTLY ROUNDED FLOATING POINT SUMMATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does this technology compare to existing methods for floating point summation?
- 1.11 What are the specific technical specifications required for implementing this technology in different computing systems?
- 1.12 Original Abstract Submitted
COMPUTATION OF CORRECTLY ROUNDED FLOATING POINT SUMMATION
Organization Name
Inventor(s)
Brett Saiki of Seattle WA (US)
William Zorn of Folsom CA (US)
Theo Drane of El Dorado Hills CA (US)
COMPUTATION OF CORRECTLY ROUNDED FLOATING POINT SUMMATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240160405 titled 'COMPUTATION OF CORRECTLY ROUNDED FLOATING POINT SUMMATION
Simplified Explanation
The patent application describes a computer computation method for correctly rounded floating point summation. The apparatus includes circuits to sort multiple floating point values, sum the values iteratively, and perform final rounding to provide a correctly rounded summation of the maximum floating point values.
- First circuit to sort and store floating point values based on exponent
- Second circuit to iteratively sum values, store accumulated value and generate intermediate output
- Third circuit to perform final rounding for correctly rounded output
Potential Applications
This technology can be applied in various fields where precise floating point summation is required, such as scientific computing, financial analysis, and machine learning algorithms.
Problems Solved
1. Ensures accurate and correctly rounded floating point summation 2. Reduces errors in calculations due to floating point precision issues
Benefits
1. Improved accuracy in floating point calculations 2. Enhanced reliability of computational results 3. Increased efficiency in handling large datasets
Potential Commercial Applications
"Accurate Floating Point Summation Technology in Financial Software"
Possible Prior Art
One possible prior art is the Kahan summation algorithm, which is a method for more accurate floating point summation by reducing numerical error accumulation during the process.
Unanswered Questions
How does this technology compare to existing methods for floating point summation?
This article does not provide a direct comparison with existing methods for floating point summation, leaving the reader to wonder about the specific advantages of this new approach.
What are the specific technical specifications required for implementing this technology in different computing systems?
The article does not delve into the technical requirements or compatibility of this technology with various computing systems, leaving a gap in understanding the practical implementation aspects.
Original Abstract Submitted
computer computation of correctly rounded floating point summation is described. an example of apparatus includes a first circuit to sort multiple floating point (fp) values based on an exponent of each fp value and store the sorted fp values in a buffer, and to provide the plurality of fp values for summation sequentially in a sorted order starting with a fp value having a smallest exponent; a second circuit to iteratively sum the fp values and store an accumulated value, generate and store a residual value representing fully resolved bits from the accumulator, and generate an intermediate output including the residual value; and a third circuit to perform final rounding of the output, the final rounded output being a correctly rounded summation of the maximum floating point values.
