This paper devises a novel adaptive framework for the energy-aware acquisition of spectrally sparse signals. The adaptive quantized compressive sensing (CS) techniques, beyond-complementary metal-oxide-semiconductor (CMOS) hardware architecture, and corresponding algorithms which utilize them have been designed concomitantly to minimize the overall cost of signal acquisition. First, a spin-based adaptive intermittent quantizer (AIQ) is developed to facilitate the realization of the adaptive sampling technique. Next, a framework for smart and adaptive determination of the sampling rate and quantization resolution based on the instantaneous signal and hardware constraints is introduced. Finally, signal reconstruction algorithms which process the quantized CS samples are investigated. Simulation results indicate that an AIQ architecture using a spin-based quantizer incurs only 20.98 μW power dissipation on average using 22-nm technology for 1-8 bits uniform output. Furthermore, in order to provide 8-bit quantization resolution, 85.302 μW maximum power dissipation is attained. Our results indicate that the proposed AIQ design provides up to 6.18-mW power savings on average compared to other adaptive rate and resolution CMOS-based CS analog-to-digital converter designs. In addition, the mean square error values achieved by the simulation results confirm efficient reconstruction of the signal based on the proposed approach
