ABSTRACT

This paper introduces EffiCSense, an architectural pathfinding framework for mixed-signal sensor front-ends for both regular and compressive sensing systems. Since sensing systems are often energy constrained, finding a suitable architecture can be a long iterative process between high-level modeling and circuit design. We present a Simulink-based framework that allows for architectural pathfinding with high-level functional models while also including power consumption models of the different circuit blocks. This allows to directly model the impact of design specifications on power consumption and speeds up the overall design process significantly. Both architectures with and without compressive sensing can be handled. The framework is demonstrated for the processing of EEG signals for epilepsy detection, comparing solutions with and without analog compressive sensing. Simulations show that using the compression, an optimal design can be found that is estimated to be 3.6 times more power-efficient compared to a system without compression, consuming 2.44μW for a detection accuracy of 99.3%.

Keywords: Compressive sensing, Sensor Front-End, Simulink, System Modeling.

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