  Pushing the frontiers of time-series information processing in ever-growing
edge devices with stringent resources has been impeded by the system's ability
to process information and learn locally on the device. Local processing and
learning typically demand intensive computations and massive storage as the
process involves retrieving information and tuning hundreds of parameters back
in time. In this work, we developed a memristor-based echo state network
accelerator that features efficient temporal data processing and in-situ online
learning. The proposed design is benchmarked using various datasets involving
real-world tasks, such as forecasting the load energy consumption and weather
conditions. The experimental results illustrate that the hardware model
experiences a marginal degradation (~4.8%) in performance as compared to the
software model. This is mainly attributed to the limited precision and dynamic
range of network parameters when emulated using memristor devices. The proposed
system is evaluated for lifespan, robustness, and energy-delay product. It is
observed that the system demonstrates a reasonable robustness for device
failure below 10%, which may occur due to stuck-at faults. Furthermore, 246X
reduction in energy consumption is achieved when compared to a custom CMOS
digital design implemented at the same technology node.
