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We experimentally demonstrate a hybrid reservoir computing system consistingof an electro-optic modulator and field programmable gate array (FPGA). Itimplements delay lines and filters digitally for flexible dynamics and highconnectivity, while supporting a large number of reservoir nodes. To evaluatethe system's performance and versatility, three benchmark tests are performed.The first is the 10th order Nonlinear Auto-Regressive Moving Average test(NARMA-10), where the predictions of 1000 and 25,000 steps yield impressivelylow normalized root mean square errors (NRMSE's) of 0.142 and 0.148,respectively. Such accurate predictions over into the far future speak to itscapability of large sample size processing, as enabled by the present hybriddesign. The second is the Santa Fe laser data prediction, where a normalizedmean square error (NMSE) of 6.73x10-3 is demonstrated. The third is the isolatespoken digit recognition, with a word error rate close to 0.34%. Accurate,versatile, flexibly reconfigurable, and capable of long-term prediction, thisreservoir computing system could find a wealth of impactful applications inreal-time information processing, weather forecasting, and financial analysis.

Efficient Reservoir Computing using Field Programmable Gate Array and Electro-optic Modulation