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DFR: An Energy-efficient Analog Delay Feedback Reservoir Computing System for Brain-inspired Computing

Neuromorphic computing, which is built on a brain-inspired silicon chip, is uniquely applied to keep pace with the explosive escalation of algorithms and data density on machine learning. Reservoir computing, an emerging computing paradigm based on the recurrent neural network with proven benefits across multifaceted applications, offers an alternative training mechanism only at the readout stage. In this work, we successfully design and fabricate an energy-efficient analog delayed feedback reservoir (DFR) computing system, which is built upon a temporal encoding scheme, a nonlinear transfer function, and a dynamic delayed feedback loop. Measurement results demonstrate its high energy efficiency with rich dynamic behaviors, making the designed system a candidate for low power embedded applications. The system performance, as well as the robustness, are studied and analyzed through the Monte Carlo simulation. The chaotic time series prediction benchmark, NARMA10, is examined through the proposed DFR computing system, and exhibits a 36%−85% reduction on the error rate compared to state-of-the-art DFR computing system designs. To the best of our knowledge, our work represents the first analog integrated circuit (IC) implementation of the DFR computing system.