Reservoir Network with Structural Plasticity for Human Activity Recognition

--The unprecedented dissemination of edge devices is accompanied by a growing demand for neuromorphic chips that can process time-series data natively without cloud support. Echo state network (ESN) is a class of recurrent neural networks that can be used to identify unique patterns in time-series data and predict future events. It is known for minimal computing resource requirements and fast training, owing to the use of linear optimization solely at the readout stage. In this work, a custom-design neuromorphic chip based on ESN targeting edge devices is proposed. The proposed system supports various learning mechanisms, including structural plasticity and synaptic plasticity, locally on-chip. This provides the network with an additional degree of freedom to continuously learn, adapt, and alter its structure and sparsity level, ensuring high performance and continuous stability. We demonstrate the performance of the proposed system as well as its robustness to noise against real-world time-series datasets while considering various topologies of data movement. An average accuracy of 95.95% and 85.24% are achieved on human activity recognition and prosthetic finger control, respectively. HE last decade has seen significant advancement in neuromorphic computing with a major thrust centered around processing streaming data using recurrent neural networks (RNNs). Despite the fact RNNs demonstrate promising performance in numerous domains including speech recognition [1], computer vision [2], stock trading [3], and medical diagnosis [4], such networks suffer from slow convergence and intensive computations [5]. In order to bypass these challenges, Jaeger and Maass suggest leveraging the rich dynamics offered by the networks' recurrent connections and random parameters and limit the training to the network advanced layers, particularly the readout layer [7]-[9]. With that, the network training and its computation complexity are significantly simplified. There are three classes of RNN networks trained using this approach known as a liquid state machine (LSM) [7], delayed-feedback reservoir [10], [11], and echo state network (ESN) which is going to be the focus of this work. ESN is demonstrated in a variety of tasks, including pattern recognition, anomaly detection [12], spatial-temporal forecasting [13], and modeling dynamic motions in bio-mimic robots [14].