# Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering

arXiv: https://arxiv.org/abs/2109.01520 · HTML: https://mlsanity.com/paper/2109.01520/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 54 references that are in the map.
- First posted on arXiv 3 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks](https://mlsanity.com/paper/1612.03940/) (2016)
- [Training Modern Deep Neural Networks for Memory-Fault Robustness](https://mlsanity.com/paper/1911.10287/) (2019)
- [Layerwise Noise Maximisation to Train Low-Energy Deep Neural Networks](https://mlsanity.com/paper/1912.10764/) (2019)
- [Optimizing the Write Fidelity of MRAMs](https://mlsanity.com/paper/2001.03803/) (2020)

## Abstract (arXiv)

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 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 so as to minimize the memory energy consumption under a 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%.