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.

Pre-2018 CSE ID: CS2009-0938

We present a novel architecture for adaptive weight calculation (AWC) that uses the QR decomposition based recursive least squares (RLS) algorithm. Our AWC core achieves a throughput of 0.20M updates per second for a 4 x 4 matrix on a Xilinx Virtex4 SX FPGA. We show that our core is significantly faster than other published FPGA implementations and it requires fewer resources. This is largely a consequence of careful error analysis that allows us to take advantage of a fixed point data representation with little degradation in the final results. Finally, our proposed architecture scales well for larger size matrices.

Pre-2018 CSE ID: CS2009-0937