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|Title:||Achieving efficient realization of Kalman Filter on CGRA through algorithm-architecture co-design||Authors:||Merchant, Farhad
Nandy, Soumitra Kumar
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2018||Source:||Merchant, F., Vatwani, T., Chattopadhyay, A., Raha, S., Nandy, S. K., & Narayan, R. (2018). Achieving efficient realization of Kalman Filter on CGRA through algorithm-architecture co-design. Proceedings of the 14th International Symposium, Applied Reconfigurable Computing 2018, 119-131. doi:10.1007/978-3-319-78890-6_10||Abstract:||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.||URI:||https://hdl.handle.net/10356/142291||ISBN:||978-3-319-78889-0||DOI:||10.1007/978-3-319-78890-6_10||Rights:||© 2018 Springer International Publishing AG, part of Springer Nature. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Conference Papers|
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