Binary Recursive Estimation on Noisy Hardware

Dupraz, Elsa; Varshney, Lav R.

doi:10.1109/isit.2019.8849626

Cited by 4 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to reduce its energy consumption, the quantized Kalman filter can be implemented on unreliable hardware [ 8 , 10 , 11 , 12 ]. Here, we assume, as in [ 10 , 12 ], that only the memory is faulty. In this case, each memory cell of a memory bank has a bit flipping probability p .…”

Section: System Modelmentioning

confidence: 99%

“…The robustness to unreliability in computation operations and memories has been investigated for several signal processing and machine-learning applications, including binary recursive estimation [ 10 ], binary linear transformation [ 11 ], deep neural networks [ 12 , 13 ], multi-agent systems [ 14 ] and distributed logistic regression [ 15 ]. Moreover, several techniques have been proposed to compensate for faults introduced by unreliable systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering

Kern

Dupraz

Bey

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

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%.

show abstract

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering

Kern

Dupraz

Bey

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…In order to reduce its energy consumption, the Kalman filter can be implemented using unreliable hardware. Here, we assume, as in [18], that only the memory is faulty. In this case, each memory cell has a bit flipping probability p. We use the model of [10] to express p with respect to the memory cell energy consumption e as p = exp(−ea) ,…”

Section: Unreliable Implementation Of the Filtermentioning

confidence: 99%

“…Therefore, in the Kalman filter, instead of having an estimate componentx we have a possibly incorrect estimate componentx. Using the binary representation given in Section 2.2, we define an energy per memory cell vector: As the filter would be particularly sensitive to a fault on the sign bit,we consider a sign-preserving model, as in [18]. This can be implemented by storing the sign bits in a reliable memory.…”

Section: Unreliable Implementation Of the Filtermentioning

confidence: 99%

“…The focus on memory is motivated by the fact that fetches from memory can consume more than a hundred times more energy than integer operations [17]. Furthermore, unreliable memories were shown to improve the energy efficiency of algorithms such as binary recursive estimation [18] and deep neural networks [19,20]. These works all rely on the statistical model of [10], which relates the level of noise in the memory cells to the energy consumption of the memory.…”

mentioning

confidence: 99%

See 1 more Smart Citation

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)

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Humans are resource-rational predictors in a sequence learning task

Ferdinand,

Yu,

Marzen

2024

Preprint

View full text Add to dashboard Cite

Organisms can solve complex tasks despite having limited cognitive resources when those resources are used optimally. Doing so optimally makes an organism resource-rational. In this paper, we show for the first time that humans are resource-rational at prediction. In a novel sequence learning experiment, participants predict data generated from hidden Markov models (HMMs) and receive online feedback via clicker training. We compute the predictive rate-accuracy (PRA) curve for each HMM to solve for the highest accuracy achievable for a given cognitive capacity, or rate, quantified as the mutual information between participants' predictions and the underlying causal state of the sequence being predicted. We found that the majority of participants achieve near-optimal prediction with various cognitive capacities, despite performing well below the maximum predictive accuracy on the task overall. We also show that this information-theoretic approach to measuring cognitive capacity can be grounded in the established psychological science concept of working memory: participants who extracted higher quantities of mutual information in the sequence learning task showed significantly higher working memory in a complex digit span test. This research provides new avenues for assessing smart behavior in difficult prediction tasks, provides a new methodology for assessing resource-rationality, and provides evidence that humans are resource-rational predictors.

show abstract

Binary Recursive Estimation on Noisy Hardware

Cited by 4 publications

References 21 publications

Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering

Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering

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

Humans are resource-rational predictors in a sequence learning task

Contact Info

Product

Resources

About