MERRC: A Memristor-Enabled Reconfigurable Low-Power Reservoir Computing Architecture at the Edge

The massive growth in the Internet of Things (IoT) has led to an increase in demand for devices receiving and transmitting data to and from the cloud during operations. Edge computing has been developed in an attempt to bring computations in proximity to the devices to overcome latency and cost overhead. IoT applications heavily reliant on machine learning (ML) tasks such as image or voice recognition can benefit from edge devices that facilitate real-time operations without costly data transmission back and forth from memory. In this work, we develop MERRC, a memristor-enabled reconfigurable architecture that incorporates processing-in-memory and reservoir computing to carry out ML tasks of image classification at the edge. This design uses a novel masking circuit to allow for image segmentation, which is combined with a delay-based reservoir to form a recurrent neural network. We further implement the final stage of classification using a fabricated memristor crossbar. Our hardware measurement results on the MNIST dataset of the delay-based reservoir with memristor crossbar arrays provide a recognition accuracy of 98%. On a more complex image classification dataset of CIFAR-10, MERRC shows a high accuracy of 88%, further highlighting the edge computing capabilities of the architecture.