  In this paper, we present efficient realization of Kalman Filter (KF) that
can achieve up to 65% of the theoretical peak performance of underlying
architecture platform. KF is realized using Modified Faddeeva Algorithm (MFA)
as a basic building block due to its versatility and REDEFINE Coarse Grained
Reconfigurable Architecture (CGRA) is used as a platform for experiments since
REDEFINE is capable of supporting realization of a set algorithmic compute
structures at run-time on a Reconfigurable Data-path (RDP). We perform several
hardware and software based optimizations in the realization of KF to achieve
116% improvement in terms of Gflops over the first realization of KF. Overall,
with the presented approach for KF, 4-105x performance improvement in terms of
Gflops/watt over several academically and commercially available realizations
of KF is attained. In REDEFINE, we show that our implementation is scalable and
the performance attained is commensurate with the underlying hardware resources
