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This paper presents a quantized Kalman filter implemented using unreliablememories. We consider that both the quantization and the unreliable memoriesintroduce errors in the computations, and develop an error propagation modelthat takes into account these two sources of errors. In addition to providingupdated Kalman filter equations, the proposed error model accurately predictsthe covariance of the estimation error and gives a relation between theperformance of the filter and its energy consumption, depending on the noiselevel in the memories. Then, since memories are responsible for a large part ofthe energy consumption of embedded systems, optimization methods are introducedso as to minimize the memory energy consumption under a desired estimationperformance of the filter. The first method computes the optimal energy levelsallocated to each memory bank individually, and the second one optimizes theenergy allocation per groups of memory banks. Simulations show a close matchbetween the theoretical analysis and experimental results. Furthermore, theydemonstrate an important reduction in energy consumption of more than 50%.

Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering