A Tuning-Free Hardware Reservoir Based on MOSFET Crossbar Array for Practical Echo State Network Implementation
Yuki Kume, Song Bian, Takashi G. Sato · 2020
Echo state network (ESN) is a class of recurrent neural network, and is known for drastically reducing the training time by the use of reservoir, a random and fixed network as the input and middle layers. In this paper, we propose a hardware implementation of ESN that uses practical MOSFET-based reservoir. As opposed to existing reservoirs that require additional tuning of network weights for improved stability, our ESN requires no post-training parameter tuning. To this end, we apply the circular law of random matrix to sparse reservoirs to determine a stable and fixed feedback gain. Through the evaluations using Mackey-Glass time-series dataset, the proposed ESN performs successful inference without post parameter tuning.