Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering

TL;DR

This paper develops an error model for quantized Kalman filters using unreliable memories, enabling energy-efficient memory allocation strategies that significantly reduce energy consumption while maintaining estimation performance.

Contribution

It introduces a novel error propagation model for unreliable memories in quantized Kalman filters and proposes optimization methods for energy-efficient memory management.

Findings

Energy consumption reduced by over 50%.

The error model accurately predicts estimation error covariance.

Optimized memory energy allocation maintains filter performance.

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 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%.