In Parallel MRI (pMRI), imaging process is accelerated by acquiring less data using multiple receiver coils and offline reconstruction algorithms (e.g. SENSitivity Encoding (SENSE)) are applied to reconstruct fully sampled data. We present a synthesizable high-description language (HDL) model of SENSE algorithm where the reconstruction can be performed within signal processing chain of MRI scanner. The proposed architecture is tested using simulated human brain data with 8 channel receiver coils and quality of reconstructed images is analyzed using artifact power. The results show that the proposed reconstruction model achieves 0.014 artifact power and is 700 times faster than the CPU based SENSE reconstruction.