Non van-Neumann Anomaly Detection in Multi-Channel Time-Series using Charge Trap Transistor Crossbars

Many of the existing techniques to detect anomalies in multi-channel time-series lack flexibility and incur significant processing-overheads; therefore, real-time, flexible anomaly detection in resource-constrained edge devices is still an open problem. Addressing the above challenges, we present an ultra-low-power non von-Neumann framework for statistical modeling based anomaly detection in multi-channel time-series. To disruptively minimize the power dissipation for anomaly detection and enhance scalability to complex time-series statistics, we pursue a co-designed approach where the anomaly statistics are modelled using an unconventional Harmonic-Mean of Gaussian-like (HMG) functions. We show that multivariate HMG functions can be implemented simply by exploiting the short-circuit current of multi-input inverters. A non-von Neumann crossbar of charge trap inverters implements a mixture of HMG function and stores model weights. On Yahoo real time-series dataset, our anomaly detection approach achieves an f1-score higher than 0.85 even in the presence of significant process variation and consumes 181fJ/sensor sample for a three-channel time-series. Compared to baseline digital and analog approaches for anomaly detection, our framework is $\sim 40 \times$ and $\sim 6 \times$ more energy efficient, respectively, while being more scalable to time-series dimension.