ADIC: Anomaly Detection Integrated Circuit in 65nm CMOS utilizing Approximate Computing

In this paper, we present a low-power anomaly detection integrated circuit (ADIC) based on a one-class classifier (OCC) neural network. The ADIC achieves low-power operation through a combination of (a) careful choice of algorithm for online learning and (b) approximate computing techniques to lower average energy. In particular, online pseudoinverse update method (OPIUM) is used to train a randomized neural network for quick and resource efficient learning. An additional 42% energy saving can be achieved when a lighter version of OPIUM method is used for training with the same number of data samples lead to no significant compromise on the quality of inference. Instead of a single classifier with large number of neurons, an ensemble of K base learner approach is chosen to reduce learning memory by a factor of K. This also enables approximate computing by dynamically varying the neural network size based on anomaly detection. Fabricated in 65nm CMOS, the ADIC has K = 7 Base Learners (BL) with 32 neurons in each BL and dissipates 11.87pJ/OP and 3.35pJ/OP during learning and inference respectively at Vdd = 0.75V when all 7 BLs are enabled. Further, evaluated on the NASA bearing dataset, approximately 80% of the chip can be shut down for 99% of the lifetime leading to an energy efficiency of 0.48pJ/OP, an 18.5 times reduction over full-precision computing running at Vdd = 1.2V throughout the lifetime.Comment: 1

Live demonstration : autoencoder-based predictive maintenance for IoT

This live demo aims to show the performance of a two-layer neural network applied to predictive maintenance. The first layer encodes features based on prior knowledge, while the second layer is trained online to detect anomalies. The system is implemented on an FPGA, acquiring real-time data from sensors attached to a motor. Faults can be triggered artificially in real-time to demonstrate anomaly detection.NRF (Natl Research Foundation, S’pore)Accepted versio

A stacked autoencoder neural network based automated feature extraction method for anomaly detection in on-line condition monitoring

Condition monitoring is one of the routine tasks in all major process industries. The mechanical parts such as a motor, gear, bearing are the major components of a process industry and any fault in them may cause a total shutdown of the whole process, which may result in serious losses. Therefore it is very crucial to predict any approaching defects before its occurrence. Several methods exist for this purpose and many research are being carried out for better and efficient models. However, most of them are based on the processing of raw sensor signals, which is tedious and expensive. Recently, there has been an increase in the feature based condition monitoring, where only the useful features are extracted from the raw signals and interpreted for the prediction of the fault. Most of these are handcrafted features, where these are manually obtained based on the nature of the raw data. This of course requires the prior knowledge of the nature of data and related processes. This limits the feature extraction process. However, recent development in the autoencoder based feature extraction method provides an alternative to the traditional handcrafted approaches; however, they have mostly been confined in the area of image and audio processing. In this work, we have developed an automated feature extraction method for on-line condition monitoring based on the stack of the traditional autoencoder and an on-line sequential extreme learning machine (OSELM) network. The performance of this method is comparable to that of the traditional feature extraction approaches. The method can achieve 100% detection accuracy for determining the bearing health states of NASA bearing dataset. The simple design of this method is promising for the easy hardware implementation of Internet of Things (IoT) based prognostics solutions.NRF (Natl Research Foundation, S’pore)Accepted versio

ADEPOS : a novel approximate computing framework for anomaly detection systems and its implementation in 65-nm CMOS

To overcome the energy and bandwidth limitations of traditional IoT systems, 'edge computing' or information extraction at the sensor node has become popular. However, now it is important to create very low energy information extraction or pattern recognition systems. In this paper, we present an approximate computing method to reduce the computation energy of a specific type of IoT system used for anomaly detection (e.g. in predictive maintenance, epileptic seizure detection, etc). Termed as Anomaly Detection Based Power Savings (ADEPOS), our proposed method uses low precision computing and low complexity neural networks at the beginning when it is easy to distinguish healthy data. However, on the detection of anomalies, the complexity of the network and computing precision are adaptively increased for accurate predictions. We show that ensemble approaches are well suited for adaptively changing network size. To validate our proposed scheme, a chip has been fabricated in UMC 65nm process that includes an MSP430 microprocessor along with an on-chip switching mode DC-DC converter for dynamic voltage and frequency scaling. Using NASA bearing dataset for machine health monitoring, we show that using ADEPOS we can achieve 8.95X saving of energy along the lifetime without losing any detection accuracy. The energy savings are obtained by reducing the execution time of the neural network on the microprocessor.National Research Foundation (NRF)This work was supported in part by Delta Electronics, Inc., and in part by the National Research Foundation Singapore under the Corp Lab@University scheme

Density of States, Carrier Concentration, and Flat Band Potential Derived from Electrochemical Impedance Measurements of N‑Doped Carbon and Their Influence on Electrocatalysis of Oxygen Reduction Reaction

Nitrogen-doped carbon (N/C) and graphene (N/G) were synthesized by the established conventional heat-treatment method, and the incorporation of nitrogen into the carbon matrix was confirmed by CHN analysis, X-ray photoelectron spectroscopy (XPS), and Raman spectroscopy. Electrochemical impedance spectroscopy (EIS) of the prepared catalysts in argon-saturated 0.1 M KOH was performed in a three-electrode rotating disk electrode (RDE) configuration. The capacitance derived from the low-frequency region of the EIS patterns was used to estimate the effective density of states [<i>D</i>(<i>E</i>_F)] of carbon and its nitrogen-doped counterparts. Moreover, the carrier concentrations (<i>N</i>_D) and flat band potentials of the samples were obtained by Mott–Schottky analysis. The metal-free catalyst samples were tested for possible oxygen reduction reaction (ORR) activity in oxygen-saturated 0.1 M KOH electrolyte, and the origin of the activity improvement with nitrogen doping of carbon/graphene can be explained on the basis of the effective density of states [<i>D</i>(<i>E</i>_F)], carrier concentration (<i>N</i>_D), and flat band potential. The results suggest that N/C-900 has the highest carrier concentration and maximum flat band potential and, therefore, the highest activity for the ORR