Impact of Derivatives Trading on Emerging Capital Markets: A Note on Expiration Day Effects in India

The impact of expiration of derivatives contracts on the underlying cash market ñ on trading volumes, returns and volatility of returns ñ has been studied in various contexts. We use an AR-GARCH model to analyse the impact of expiration of derivatives contracts on the cash market at the largest stock exchange in India, an important emerging capital market. Our results indicate that trading volumes were significantly higher on expiration days and during the five days leading up to expiration days (“expiration weeks”), compared with nonexpiration days (weeks). We also find significant expiration day effects on daily returns to the market index, and on the volatility of these returns. Finally, our analysis indicates that it might be prudent to undertake analysis of expiration day effects (or other events) using methodologies that model the underlying data generating process, rather than depend on comparison of mean and median alone.http://deepblue.lib.umich.edu/bitstream/2027.42/57243/1/wp863 .pd

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

Edge processing in IoT using approximate and in-memory computing

With a large number of sensors getting connected to the internet, scalability of Internet of Things (IoT) has started to hinge on Edge computing-the ability to partly process the raw data at the sensor on the edge of the network instead of transmitting all data to the cloud. However, sensor nodes are typically highly power-constrained due to the limited battery and also requires a long lifetime due to difficulties in replacing nodes in many applications. Hence, this thesis focuses on using different circuit and algorithmic techniques in particular approximate computing, near and in-memory computing (IMC), dynamic voltage and frequency scaling (DVFS) to reduce the energy consumption of edge devices in the Internet of Things. As a first example, we choose predictive maintenance (PdM), one of the most important applications pertaining to IoT in Industry 4.0. Machine learning is used to predict the failure of a machine before the actual event occurs. However, the main challenges in PdM are (a) lack of enough data from failing machines to train binary classifi ers, and (b) paucity of power and bandwidth to transmit sensor data to cloud throughout the lifetime of the machine. In our work, we propose an anomaly detection scheme that can be trained only using healthy machine data. Our Anomaly Detection based Power Saving (ADEPOS) scheme is aimed at saving energy by using approximate computing through the lifetime of the machine. At the beginning of the machine's life, low accuracy computations are used when the probability of the machine being healthy is high. However, on the detection of anomalies, as time progresses, the anomaly detector is switched to higher accuracy modes. Reduction in computation accuracy may be achieved in many ways, such as reducing the number of neurons, reducing the bit width of data, dynamic voltage frequency scaling, etc. Tested on the NASA bearing dataset, ADEPOS demonstrates up to 8.8x reduction of neurons on average over the lifetime of bearings. This resulted in 8.95x energy saving for microprocessor implementation and ~18.8x energy saving in an ASIC implementation, both in 65nm CMOS. The second part of this research explores the near and in-memory computing (IMC) to reduce the data movement between the storage and processing elements for video processing in the application of traffic surveillance. Generally, image frames from a camera undergo image denoising, region proposal, object classi cation, and object tracking steps for traffic surveillance and monitoring. However, a realization of this data-intensive computing following traditional von Neumann architecture involves a higher energy dissipation and more substantial execution time due to the enormous data movement between computing and storage units. Further, for stationary cameras, there exists signi cant temporal redundancy which can be exploited by event-driven or neuromorphic vision sensors (NVS) that report data only when there is activity in the scene. However, due to the presence of noise, NVS pixels report events even in the absence of actual activity. In this dissertation, a 6T-SRAM in-memory computing based image denoising for event-based binary image (EBBI) frame from a neuromorphic vision sensor (NVS) is presented. We suggest a nonoverlap median lter (NOMF), an approximation of a traditional median lter for image denoising. The NOMF enables us to implement image denoising leveraging the inherent read disturb phenomenon of the 6T-SRAM. Besides, detecting zero frames is easily done by IMC techniques tracking bit line voltage during ltering operation and this can be used now to shut off the rest of the processor for ~2x energy bene ts in urban traffic settings. Fabricated in 65nm CMOS, this chip produces denoised frames with an energy efficiency of 51.3 TOPS/W and a peak throughput of 134.4 GOPS at 70MHz. As a next step, we propose a 9T-SRAM near and in-memory computing based region proposal network for the event-based binary image frame to exploit spatial redundancy in the valid frames. The region proposal network nds out the bounding box encapsulating of an object which reduces the computation of an object recognition deep neural network (DNN) by con ning the computing region surrounding the object instead of the whole image frame. The proposed 9T-SRAM cell enables a 1-D projection of objects on the horizontal and vertical axes of an image. An iterative and selective search of the rising and falling edges of 1-D projection yields the coordinates of a bounding box encapsulating an object. Simulated in 65nm CMOS, this chip produces up to 16 region proposals per frame and achieves ~682x energy savings compared to the digitally implemented connected component labeling (CCL) algorithm and throughput of 1.17 frames/usec at 200MHz. In summary, we presented a set of algorithms and hardware solutions for energy efficient edge computing that use approximate and in-memory compute techniques. We have demonstrated the results in two different applications of predictive maintenance and traffic monitoring.Doctor of Philosoph

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

Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics

Sensory information processing in robots relies on a centralized approach with issues of wiring, fault-tolerance and latency. Here, the authors report a decentralized neuromorphic approach with self-healable memristive elements enabling intelligent sensations in a prototypical robotic nervous system

Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks

Accomplishing complex cognitive tasks such as speech recognition calls for artificial intelligence hardware with high computing precision. John et al. propose deep recurrent neural networks based on optoelectronic transition metal dichalcogenide memristors with high weight precision for in-memory computing