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FPGA Implementation of Tri-matrix Multiplication Accelerator using Circulant Matrices for Kalman Filters

Mohan Pudi, Pathipati Srihari, Bethi Pardhasaradhi

20222022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon)10 citationsDOI

Abstract

The Kalman filter (KF) algorithm’s low-power and low-area implementations are essential for both civilian and military applications. In KF, the tri-matrix multiplication (PGP and PGP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> ) consumes more cycles and transposition buffer hardware. Hence there is a strong need to develop an accelerator module to compute the tri-matrix multiplication without the transposition buffer module and in fewer cycles. This work presents an algorithm for direct or transposed tri-matrix multiplication without timing penalty and extra transposition buffer hardware unit. For an N-dimensional matrix multiplication (PG), the data is stored in circulant matrix form with N BRAMs for ease of write/read, and the resultant is stored in circulant form to enable the chained operation. The complexity of intermediate output (PG) and tri-matrix multiplication (PGP) complexity are $\mathcal{O}\left( {{N^2}} \right){\text{ and }}\mathcal{O}\left( {2{N^2}} \right)$ respectively. The KF algorithm with different state vectors is considered, and the tri-matrix multiplications are accelerated on NEXYS 4 DDR Artix-7 FPGA. The critical operating frequency for 2-D constant velocity (CV) and constant acceleration (CA) models operating with 200 MHz and 151 Mz, respectively. In contrast, the 3-D CV and CA models operate with 151 MHz and 115 MHz, respectively.

Topics & Concepts

Circulant matrixField-programmable gate arrayMultiplication (music)Kalman filterComputer scienceMatrix multiplicationMatrix (chemical analysis)Matrix algebraArithmeticParallel computingMathematicsAlgorithmComputer hardwareCombinatoricsEigenvalues and eigenvectorsArtificial intelligenceChemistryPhysicsQuantum mechanicsQuantumChromatographyNumerical Methods and AlgorithmsMatrix Theory and AlgorithmsParallel Computing and Optimization Techniques