Arithmatica claimed it could improve silicon performance and reduce die area with its innovations in mathematical circuit blocks has revealed that Xilinx Iand Nvidia are two companies that have used its CellMath libraries. Nvidia has used the technology with NV40. Applying the libraries to math-critical blocks in graphics chips can reduce overall chip area by up to 10 percent and can improve performance in processor designs to varying degrees, depending on the application. “Using Arithmatica’s CellMath Graphics Library, we were able to achieve a new threshold of performance in our next-generation of graphics processors while reducing the chip area dedicated to calculations in many of our major blocks by typically 20-30 percent,” said Gopal Solanki, vice president of platform products at Nvidia.

Arithmatica claimed it could improve silicon performance and reduce die area with its innovations in mathematical circuit blocks has revealed that Xilinx Iand Nvidia are two companies that have used its CellMath libraries. Nvidia has used the technology with NV40. Applying the libraries to math-critical blocks in graphics chips can reduce overall chip area by up to 10 percent and can improve performance in processor designs to varying degrees, depending on the application. “Using Arithmatica’s CellMath Graphics Library, we were able to achieve a new threshold of performance in our next-generation of graphics processors while reducing the chip area dedicated to calculations in many of our major blocks by typically 20-30 percent,” said Gopal Solanki, vice president of platform products at Nvidia.