Low Power Unsupervised Anomaly Detection by Nonparametric Modeling of Sensor Statistics

Abstract

This work presents AEGIS, a novel mixed-signal framework for real-time anomaly detection by examining sensor stream statistics. AEGIS utilizes Kernel Density Estimation (KDE)-based non-parametric density estimation to generate a real-time statistical model of the sensor data stream. The likelihood estimate of the sensor data point can be obtained based on the generated statistical model to detect outliers. We present CMOS Gilbert Gaussian cell-based design to realize Gaussian kernels for KDE. For outlier detection, the decision boundary is defined in terms of kernel standard deviation (σ Kernel ) and likelihood threshold (P T hres ). We adopt a sliding window to update the detection model in real-time. We use time-series dataset provided from Yahoo to benchmark the performance of AEGIS. A f1-score higher than 0.87 is achieved by optimizing parameters such as length of the sliding window and decision thresholds which are programmable in AEGIS. Discussed architecture is designed using 45nm technology node and our approach on average consumes ∼75 µW power at a sampling rate of 2 MHz while using ten recent inlier samples for density estimation. Fullversion of this research has been published at IEEE TVLSI

Ultralow-Power Localization of Insect-Scale Drones: Interplay of Probabilistic Filtering and Compute-in-Memory

“…4 is much simplified. While a digital implementation to evaluate multivariate Gaussian will require multiplier, adder, and look-up tables for exponential or log-ADD function [39]- [41], HMG in Fig. 4 is implemented using only six transistors and can be evaluated by applying analog voltages V X , V Y , and V Z .…”

Analog Gaussian Function Circuit: Architectures, Operating Principles and Applications

“…In most cases, digital or other analog circuits are attached around a partially tunable Gaussian or Gaussian-like function circuit (Figure 28) to improve the tunability of the Gaussian function curve's three characteristics (height, mean value and variance). The extra components include multiplexers (MUX) [11,81] and/or switches [15,[82][83][84][85] and other digital circuitry (mixed-mode architectures [86]) [82][83][84], series of resistors [19,87,88], DACs [22,28,87] or Analog to Digital converters (ADC) [22], multipliers [28,31,33,87] or tunable current mirrors [89,90], OTAs [91][92][93][94] or other amplifiers [12,87], common mode feedback circuits (CMFB) [22,95], squarers [33], exponentiators [87], current-controlled current-conveyor second generation (CCII) circuits [90], minimum value circuits [96] and additional current correlators [97]. Four representative examples are provided in Figures 29-32.…”

“…In particular, multiplexers and switches are used to select the appropriate value from multiple parallel outputs in order to achieve the tunability in the variance [11,15,[81][82][83][84][85]. In a similar manner, the series of resistors alter the Gaussian function output by changing the total resistance value [19,87,88]. Moreover, DACs, multipliers, squarers or tunable current mirrors usually directly affect the height of the Gaussian function [22,28,31,33,87,89,90].…”

“…Gaussian function circuits are used as a building block for the implementation of the detector in smart sensor systems. Specifically, two exemplary implementations exist in the literature [19,20], to the best of the author's knowledge, and their characteristics are summarized in Table 9. The first [19] is a mixed-mode real-time anomaly detection system for sensor stream statistics, shown in Figure 40.…”

“…Since the first Gaussian function circuit (also called the Bump circuit) was introduced by Delbruck (Delbruck's Simple Bump) in 1991 [1,2] (shown in Figure 1), many research groups have focused on improving this architecture and/or incorporating it in various fields [3][4][5][6][7][8][9][10][11][12][13][14]. Some of the implementations are used for Machine Learning (ML) applications [15,16], neuromorphic systems [17,18], smart sensors [19,20] and fuzzy or neuro-fuzzy systems [21,22]. Gaussian function circuits are specific analog circuits that provide an output current, which is a typical Gaussian function [23] or a bell-shaped curve [24].…”