Exploring Architectural Solutions for an Energy-Efficient Kalman Filter Gain Realization

Pereira, Pedro T. L.; Paim, Guilherme; Ucker, Patricia; Costa, Eduardo; Almeida, Sérgio; Bampi, Sérgio

doi:10.1109/icecs46596.2019.8964734

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…We then introduce the following optimization problem: where ≺ is a component-wise inequality between the two matrices, and where the minimum is taken over all the energy vectors e as defined in (9). The value e thres is the minimum level of energy on each memory cell, so as to avoid undesired effects such as circuit delays and energy leakage [10].…”

Section: Optimal Energy Allocationmentioning

confidence: 99%

“…Kalman filtering can be quite computationally intensive and most existing work focus on reducing its complexity and making it more computationally efficient, see e.g. [7,8] for FPGA implementations, and [9] for a recent ASIC implementation. Generally speaking, an approach to decrease the energy consumption of a hardware implementation consists of reducing its power supply [10].…”

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confidence: 99%

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Improving the Energy-Efficiency of a Kalman Filter Using Unreliable Memories

Kern

Dupraz

Bey

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Kalman filters are widely used for real-time estimation of dynamic systems, and they sometimes need to be implemented on energy-constrained devices. A Kalman filter implementation from unreliable memories is considered, where the flipping probability of a bit in a memory cell directly depends on its energy consumption. The degradation in estimation performance caused by the noise in the memory is theoretically investigated. Updated equations are then developed for the Kalman filter, taking into account the new source of noise from the unreliable memory. Finally, a method is proposed to optimize the bit energy allocation in the memory, and it is shown from numerical simulations that this method allows for important energy gains.

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Section: Optimal Energy Allocationmentioning

confidence: 99%

mentioning

confidence: 99%

Improving the Energy-Efficiency of a Kalman Filter Using Unreliable Memories

Kern

Dupraz

Bey

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

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“…Although these models are not relevant for characterizing the effect of unreliable memories, the main lessons they provide are that Kalman filtering is very sensitive to inaccuracies and that one should re-derive the optimal Kalman filter depending on the specifically considered uncertainty model. On a different line of research, other prior works aim at reducing the energy requirements for Kalman filtering by focusing on reduced computational complexity in field-programmable gate arrays (FGPAs) [ 25 , 26 ] and application-specific integrated circuits (ASICs) [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering

Kern

Dupraz

Bey

et al. 2022

Sensors

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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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