We present a novel scalable architecture for matrix inversion that uses the modified Gram-Schmidt algorithm based on QR decomposition. Our core achieves a throughput of 0.18M updates per second for a 4 x 4 matrix using 19 bits of precision on a Xilinx Virtex4 SX FPGA. We also present two different designs which use longer data lines, 26 and 32 bits, and compare our results with another matrix inversion architecture which is the only scalable approach so far. We show that our core is significantly faster than the other published FPGA implementation as it requires fewer resources due to the usage of fixed point arithmetic and an effective resource utilization. We show that our proposed architecture is scalable by presenting the results for 6 x 6 and 8 x 8 matrices.
The authors of these documents have submitted their reports to this technical report series for the purpose of non-commercial dissemination of scientific work. The reports are copyrighted by the authors, and their existence in electronic format does not imply that the authors have relinquished any rights. You may copy a report for scholarly, non-commercial purposes, such as research or instruction, provided that you agree to respect the author's copyright. For information concerning the use of this document for other than research or instructional purposes, contact the authors. Other information concerning this technical report series can be obtained from the Computer Science and Engineering Department at the University of California at San Diego, email@example.com.
[ Search ]