Convolutional Echo-State Network with Random Memristors for Spatiotemporal Signal Classification

The unprecedented development of Internet of Things results in the explosion of spatiotemporal signals generated by smart edge devices, leading to a surge of interest in real-time learning of such data. This imposes a big challenge to conventional digital hardware because of physically separated memory and processing units and the transistor scaling limit. Memristors are deemed a solution for efficient and portable deep learning. However, their ionic resistive switching incurs large programming stochasticity and energy, compromising their advantages in real-time learning spatiotemporal signals. To address the aforementioned issues, we propose a novel hardware-software codesign. Hardware-wise, the stochasticity in memristor programming is leveraged to produce random matrices for efficient in-memory computing. Software-wise, random convolutional-pooling architectures are integrated with echo-state networks that compute with the hardware random matrices and make real-time learning affordable. The synergy of the hardware and software not only improves the performance over conventional echo-state networks, that is, 90.94% and 91.67% (compared to baselines 88.33% and 62.50%), but also retains 187.79x and 93.66x improvement of energy efficiency compared to the digital alternatives on the representative Human Activity Recognition Using Smartphones (HAR) and CRICKET datasets, respectively. These advantages make random convolutional echo-state network (RCESN) a promising solution for the future smart edge hardware.