What every computer scientist should know about floating-point arithmetic
ACM Computing Surveys (CSUR)
What do we need beyond IEEE arithmetic?
Computer arithmetic and self-validating numerical methods
Industrial applications of interval techniques
Computer arithmetic and self-validating numerical methods
Parallel reduced area multipliers
Journal of VLSI Signal Processing Systems - Special issue on application-specific array processors
A Residue Arithmetic Extension for Reliable Scientific Computation
IEEE Transactions on Computers
Pascal-XSC: Language Reference with Examples
Pascal-XSC: Language Reference with Examples
IEEE Micro
A Priori Worst Case Error Bounds for Floating-Point Computations
IEEE Transactions on Computers
A New VLSI Vector Arithmetic Coprocessor for the PC
ARITH '95 Proceedings of the 12th Symposium on Computer Arithmetic
Hardware Design and Arithmetic Algorithms for a Variable-Precision, Interval Arithmetic Coprocessor
ARITH '95 Proceedings of the 12th Symposium on Computer Arithmetic
A Combined Interval and Floating Point Multiplier
GLS '98 Proceedings of the Great Lakes Symposium on VLSI '98
A variable-precision, interval arithmetic processor
A variable-precision, interval arithmetic processor
CORDIC Processor for Variable-Precision Interval Arithmetic
Journal of VLSI Signal Processing Systems
A monte-carlo floating-point unit for self-validating arithmetic
Proceedings of the 19th ACM/SIGDA international symposium on Field programmable gate arrays
The Krawczyk algorithm: rigorous bounds for linear equation solution on an FPGA
ARC'11 Proceedings of the 7th international conference on Reconfigurable computing: architectures, tools and applications
FPGA implementation of variable-precision floating-point arithmetic
APPT'11 Proceedings of the 9th international conference on Advanced parallel processing technologies
An optimizing compiler for automatic shader bounding
EGSR'10 Proceedings of the 21st Eurographics conference on Rendering
The Journal of Supercomputing
| Hi-index | 14.98 |
Traditional computer systems often suffer from roundoff error and catastrophic cancellation in floating point computations. These systems produce apparently high precision results with little or no indication of the accuracy. This paper presents hardware designs, arithmetic algorithms, and software support for a family of variable-precision, interval arithmetic processors. These processors give the programmer the ability to detect and, if desired, to correct implicit errors in finite precision numerical computations. They also provide the ability to solve problems that cannot be solved efficiently using traditional floating point computations. Execution time estimates indicate that these processors are two to three orders of magnitude faster than software packages that provide similar functionality.