An Area-Efficient Implementation of Recurrent Neural Network Core for Unsupervised Anomaly Detection
Takuya Sakuma, Hiroki Matsutani · 2020
Toward on-device anomaly detection for time-series data, in this paper, we analyze Echo State Network (ESN), which is a simple form of Recurrent Neural Networks (RNNs), and propose its area-efficient implementation. It is evaluated in terms of the anomaly detection capability and area. (Keywords: On-device learning, Machine learning, and Anomaly detection).