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The echo state network (ESN) is a recently developed machine-learning paradigm whose processing capabilities rely on the dynamical behavior of recurrent neural networks. Its performance outperforms traditional recurrent neural networks in nonlinear system identification and temporal information processing applications. We design and implement a cost-efficient ESN architecture on field-programmable gate array (FPGA) that explores the full capacity of digital signal processor blocks on low-cost and low-power FPGA hardware. Specifically, our scalable ESN architecture on FPGA exploits Xilinx DSP48E1 units to cut down the need of configurable logic blocks. The proposed architecture includes a linear combination processor with negligible deployment of configurable logic blocks and a high-accuracy nonlinear function approximator. Our work is verified with the prediction task on the classical NARMA dataset …