Singular Spectral Analysis (SSA) is a computationally intensive approach to denoise and detect a time-series signal. It requires the evaluation of eigenvalues and eigenvectors of a

covariance matrix, which is the computationally intensive step in the SSA algorithm. The current work presents a feasible approach to implement the algorithm in embedded hardware using a PYNQ-Z2 Field Programmable Gate Array (FPGA) board. We implemented the algorithm using both the Processing System (PS) and the Programmable Logic (PL) of the PYNQ-Z2 System on Chip (SoC) with the help of a High-Level Synthesis (HLS) tool. A case study is carried out on a Nuclear Quadruple Resonance (NQR) signal. The implementation result demonstrates a hardware acceleration of 15.48x with respect to the equivalent software implementation of the algorithm on the  ARM Cortex-A9 processor.