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Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering

Jonathan Kern 1, 2, 3 Elsa Dupraz 1, 4 Abdeldjalil Aissa El Bey 1, 2 Lav Varshney 5 François Leduc-Primeau 3
2 Lab-STICC_COSYDE - Equipe Communication System Design
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance : UMR6285
4 Lab-STICC_CODES - Equipe CODES
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance : UMR6285
Abstract : This paper presents a quantized Kalman filter implemented using unreliable memories. We consider that both the quantization and the unreliable memories introduce errors in the computations, and we develop an error propagation model that takes into account these two sources of errors. In addition to providing updated Kalman filter equations, the proposed error model accurately predicts the covariance of the estimation error and gives a relation between the performance of the filter and its energy consumption, depending on the noise level in the memories. Then, since memories are responsible for a large part of the energy consumption of embedded systems, optimization methods are introduced to minimize the memory energy consumption under the desired estimation performance of the filter. The first method computes the optimal energy levels allocated to each memory bank individually, and the second one optimizes the energy allocation per groups of memory banks. Simulations show a close match between the theoretical analysis and experimental results. Furthermore, they demonstrate an important reduction in energy consumption of more than 50%.
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https://hal-imt.archives-ouvertes.fr/hal-03540228
Contributor : Abdeldjalil Aïssa-El-Bey Connect in order to contact the contributor
Submitted on : Sunday, January 23, 2022 - 3:23:32 PM
Last modification on : Monday, April 4, 2022 - 9:28:32 AM
Long-term archiving on: : Sunday, April 24, 2022 - 6:06:55 PM

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Jonathan Kern, Elsa Dupraz, Abdeldjalil Aissa El Bey, Lav Varshney, François Leduc-Primeau. Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering. Sensors, MDPI, 2022, Special Issue Machine Learning, Signal, and/or Image Processing Methods to Enhance Environmental Sensors, 22 (3), pp.853. ⟨10.3390/s22030853⟩. ⟨hal-03540228⟩

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