Echo State Network (ESN) is a recently developed machine-learning paradigm whose processing capabilities rely on the dynamical behavior of recurrent neural networks (RNNs). Its performance metrics outperform traditional RNNs in nonlinear system identification and temporal information processing. In this thesis, we design and implement ESNs through Field-programmable gate array (FPGA) and explore their full capacity of digital signal processors (DSPs) to target low-cost and low-power applications. We propose a cost-optimized and scalable ESN architecture on FPGA, which exploits Xilinx DSP48E1 units to cut down the need of configurable logic blocks (CLBs). The proposed work includes a linear combination processor with negligible deployment of CLBs, as well as a high-accuracy non-linear function approximator, both with the help of only 9 DSP units in each neuron. The architecture is verified with the classical NARMA dataset, and a symbol detection task for an orthogonal frequency division multiplexing (OFDM) system on a wireless communication testbed. In the worst-case scenario, our proposed architecture delivers a matching bit error rate (BER) compares to its corresponding software ESN implementation. The performance difference between the hardware and software approach is less than 6.5%. The testbed system is built on a software-defined radio (SDR) platform, showing that our work is capable of processing the real-world data. / Master of Science / Machine learning is a study of computer algorithms that evolves itself by learning through experiences. Currently, machine learning thrives as it opens up promising opportunities of solving the problems that is difficult to deal with conventional methods. Echo state network (ESN), a recently developed machine-learning paradigm, has shown extraordinary effectiveness on a wide variety of applications, especially in nonlinear system identification and temporal information processing. Despite the fact, ESN is still computationally expensive on battery-driven and cost-sensitive devices. A fast and power-saving computer for ESN is desperately needed. In this thesis, we design and implement an ESN computational architecture through the field-programmablegate array (FPGA). FPGA allows designers to build highly flexible customized hardware with rapid development time. Our design further explores the full capacity of digital signal processors (DSP) on Xilinx FPGA to target low-cost and low-power applications. The proposed cost-optimized and scalable ESN architecture exploits Xilinx DSP48E1 units to cut down the need of configurable logic blocks (CLBs). The work includes a linear combination processor with negligible deployment of CLBs, and a high-accuracy non-linear function approximator, both with the help of only 9 DSP units in each neuron. The architecture is verified with the classical NARMA dataset, and a symbol detection task for an orthogonal frequency division multiplexing (OFDM) system in a wireless communication testbed. In the worst-case scenario, our proposed architecture delivers a matching bit error rate (BER) compares to its corresponding software ESN implementation. The performance difference between the hardware and software approach is less than 6.5%. The testbed system is built on a software-defined radio (SDR) platform, showing that our work is capable of processing the real-world data.