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%.
翻译:本文展示了使用不可靠的记忆实施的量化的卡尔曼过滤器。 我们认为量化和不可靠的记忆都会在计算中引入错误, 并开发出一个考虑到这两个错误源的错误传播模型。 除了提供更新的卡尔曼过滤方程式外, 拟议的错误模型准确预测了估算错误的共变性, 并给出了过滤器性能和能量消耗之间的关系, 取决于记忆中的噪音水平。 然后, 由于记忆是嵌入系统大部分能源消耗的原因, 因此引入了优化方法, 以便根据过滤器的预期估计性能最大限度地减少内存能源消耗。 第一个方法计算了分配给每个记忆库的最佳能量水平, 第二个方法优化了每个记忆库组合的能量分配。 模拟显示, 理论分析与实验结果之间非常接近。 此外, 模拟显示能源消耗率显著下降, 超过 50% 。