Energy efficiency is one of the most concerns in tele-monitoring. As the rapid development of mobile technology, wireless communication has gradually become the biggest energy sector in most tele-monitoring applications. Recently, Compressed Sensing (CS) has attracted increasingly attention to solve this problem due to its extremely low sampling rate. In this paper, we investigate the quantization effect in the Compressed Sensing architecture. We point out that quantization configuration is a critical factor towards the energy efficiency concern of the entire CS architecture. To this end, we present a configurable Quantized Compressed Sensing (QCS) Architecture, where sampling rate and quantization are jointly explored for better energy-efficiency. Furthermore, to overcome the computational complexity of the configuration procedure, we propose a rapid configuration algorithm, called RapQCS, to promote the configuration speed. Through the experiments with public physiological data, the configurable QCS architecture can gain more than 60% performance-energy trade-off than the constant QCS architecture. Furthermore, our proposed RapQCS algorithm can achieve more than 200× speedup on average, while only decreasing the reconstructed signal fidelity by 1.75%.