A Deep Dive into the Computational Fidelity of High Variability Low Energy Barrier Magnet Technology for Accelerating Optimization and Bayesian Problems

Low energy barrier magnet (LBM) technology has recently been proposed as a candidate for accelerating algorithms based on energy minimization and probabilistic graphs because their physical characteristics have a one-to-one mapping onto the primitives of these algorithms. Many of these algorithms have a much higher tolerance for error compared to high-accuracy numerical computation. LBM, however, is a nascent technology, and devices show high sample-to-sample variability. In this work, we take a deep dive into the overall fidelity afforded by this technology in providing computational primitives for these algorithms. We show that while the compute results show finite deviations from zero variability devices, the margin of error is almost always certifiable to a certain percentage. This suggests that LBM technology could be a viable candidate as an accelerator for popular emerging paradigms of computing.Comment: 5 pages, 8 figure

Interfacing Relationship among Work Stress, Service Quality and Customer Satisfaction: Evidence from Banking Industry of Bangladesh

The performance of the employees serving at different levels of an organization gets directly affected by work stress which is also associated with employee motivation and customer satisfaction. Many scholars have argued on the previous comment. The present study's primary purpose is to determine the interfacing relationship among work stress, quality of services, and customer satisfaction of Bangladesh's banking industries. The study mainly followed a quantitative research method based on survey technique. The study has been completed in two sections. The first one was designed for bank employees to measure job stress and job satisfaction. The second one was for customers to investigate their satisfaction level on selected banks. Using the SERVQUAL model responses on service quality and customer satisfaction were collected. A total of 200 employees of the sampled banks were selected conveniently from 40 branches. Subsequently, 110 customers of the sampled Bank were selected randomly from bank premises. Besides, a total of 130 customers were selected purposively based on the contact number collected from the Bank, and the link of the google survey was sent to the selected customers but only 90 responses were collected out of the 130 respondents. Collected data were analyzed by using SPSS and Spreadsheet. Job stress and employee satisfaction were measured with the Kahn et al. (1964) instrument by descriptive statistics. The SERVQUAL model of Parasuraman et al. (1988) was applied to measure the quality of service and satisfaction level of customers.The present study found that bank employees are not satisfied with four dimensions: Governance, working environment, structure and facilities, benefits, etc. Subsequently, the SERVQUAL model analysis found that customers are not satisfied at all on all dimensions. The study concludes with having a positive relationship between the stress level of employees, quality of service, and satisfaction level of the customer within Bangladesh's banking industry. Keywords: Work stress, Service Quality, Customer Satisfaction, SERVQUAL model] DOI: 10.7176/JESD/12-4-04 Publication date: February 28th 202

Choose your tools carefully: a comparative evaluation of deterministic vs. stochastic and binary vs. analog neuron models for implementing emerging computing paradigms

Neuromorphic computing, commonly understood as a computing approach built upon neurons, synapses, and their dynamics, as opposed to Boolean gates, is gaining large mindshare due to its direct application in solving current and future computing technological problems, such as smart sensing, smart devices, self-hosted and self-contained devices, artificial intelligence (AI) applications, etc. In a largely software-defined implementation of neuromorphic computing, it is possible to throw enormous computational power or optimize models and networks depending on the specific nature of the computational tasks. However, a hardware-based approach needs the identification of well-suited neuronal and synaptic models to obtain high functional and energy efficiency, which is a prime concern in size, weight, and power (SWaP) constrained environments. In this work, we perform a study on the characteristics of hardware neuron models (namely, inference errors, generalizability and robustness, practical implementability, and memory capacity) that have been proposed and demonstrated using a plethora of emerging nano-materials technology-based physical devices, to quantify the performance of such neurons on certain classes of problems that are of great importance in real-time signal processing like tasks in the context of reservoir computing. We find that the answer on which neuron to use for what applications depends on the particulars of the application requirements and constraints themselves, i.e., we need not only a hammer but all sorts of tools in our tool chest for high efficiency and quality neuromorphic computing

A True Random Number Generator for Probabilistic Computing using Stochastic Magnetic Actuated Random Transducer Devices

Magnetic tunnel junctions (MTJs), which are the fundamental building blocks of spintronic devices, have been used to build true random number generators (TRNGs) with different trade-offs between throughput, power, and area requirements. MTJs with high-barrier magnets (HBMs) have been used to generate random bitstreams with $\lesssim$ 200~Mb/s throughput and pJ/bit energy consumption. A high temperature sensitivity, however, adversely affects their performance as a TRNG. Superparamagnetic MTJs employing low-barrier magnets (LBMs) have also been used for TRNG operation. Although LBM-based MTJs can operate at low energy, they suffer from slow dynamics, sensitivity to process variations, and low fabrication yield. In this paper, we model a TRNG based on medium-barrier magnets (MBMs) with perpendicular magnetic anisotropy. The proposed MBM-based TRNG is driven with short voltage pulses to induce ballistic, yet stochastic, magnetization switching. We show that the proposed TRNG can operate at frequencies of about 500~MHz while consuming less than 100~fJ/bit of energy. In the short-pulse ballistic limit, the switching probability of our device shows robustness to variations in temperature and material parameters relative to LBMs and HBMs. Our results suggest that MBM-based MTJs are suitable candidates for building fast and energy-efficient TRNG hardware units for probabilistic computing.Comment: 10 pages, 10 figures, Accepted at ISQED 2023 for poster presentatio

Phase Change Induced Magnetic Switching through Metal-insulator Transition in VO2/TbFeCo Films

The ability to manipulate spins in magnetic materials is essential in designing spintronics devices. One method for magnetic switching is through strain. In VO2 on TiO2 thin films, while VO2 remains rutile across the metal-insulator transition, the in-plane lattice area expands going from low temperature insulating phase to high temperature conducting phase. In a VO2/TbFeCo bilayer, the expansion of the VO2 lattice area exerts tension on the amorphous TbFeCo layer. Through the strain effect, magnetic properties, including the magnetic anisotropy and magnetization, of TbFeCo can be changed. In this work, the changes in magnetic properties of TbFeCo on VO2/TiO2(011) are demonstrated using anomalous Hall effect measurements. Across the metal-insulator transition, TbFeCo loses perpendicular magnetic anisotropy, and the magnetization in TbFeCo turns from out-of-plane to in-plane. Using atomistic simulations, we confirm these tunable magnetic properties originating from the metal-insulator transition of VO2. This study provides the groundwork for controlling magnetic properties through a phase transition.Comment: To be published in Nanomaterial

Reduced sensitivity to process, voltage and temperature variations in activated perpendicular magnetic tunnel junctions based stochastic devices

True random number generators (TRNGs) are fundamental building blocks for many applications, such as cryptography, Monte Carlo simulations, neuromorphic computing, and probabilistic computing. While perpendicular magnetic tunnel junctions (pMTJs) based on low-barrier magnets (LBMs) are natural sources of TRNGs, they tend to suffer from device-to-device variability, low speed, and temperature sensitivity. Instead, medium-barrier magnets (MBMs) operated with nanosecond pulses - denoted, stochastic magnetic actuated random transducer (SMART) devices - are potentially superior candidates for such applications. We present a systematic analysis of spin-torque-driven switching of MBM-based pMTJs (Eb ~ 20 - 40 kBT) as a function of pulse duration (1 ps to 1 ms), by numerically solving their macrospin dynamics using a 1-D Fokker-Planck equation. We investigate the impact of voltage, temperature, and process variations (MTJ dimensions and material parameters) on the switching probability of the device. Our findings indicate SMART devices activated by short-duration pulses (< 1 ns) are much less sensitive to process-voltage-temperature (PVT) variations while consuming lower energy (~ fJ) than the same devices operated with longer pulses. Our results show a path toward building fast, energy-efficient, and robust TRNG hardware units for solving optimization problems.Comment: 7 pages, 5 figure

Crouzon syndrome: an experience of surgical intervention at Bangabandhu Sheikh Mujib Medical University, Bangladesh

Crouzon syndrome, also called craniofacial dysostosis is a rare autosomal dominant disease. It has a prevalence of 1 in 25,000 live births and it constitutes 4.8% of all craniosynostosis. Craniosynostosis, shallow orbits, maxillary hypoplasia, ocular proptosis and hypertelorism are the cardinal features of Crouzon syndrome. Here, the authors report a case of this rare entity who presented with brachycephaly, maxillary hypoplasia, shallow orbit, hypertelorism and bilateral proptosis. This patient’s overall management was done with the involvement of a multidisciplinary team

Choose your tools carefully: A Comparative Evaluation of Deterministic vs. Stochastic and Binary vs. Analog Neuron models for Implementing Emerging Computing Paradigms

Neuromorphic computing, commonly understood as a computing approach built upon neurons, synapses, and their dynamics, as opposed to Boolean gates, is gaining large mindshare due to its direct application in solving current and future computing technological problems, such as smart sensing, smart devices, self-hosted and self-contained devices, artificial intelligence (AI) applications, etc. In a largely software-defined implementation of neuromorphic computing, it is possible to throw enormous computational power or optimize models and networks depending on the specific nature of the computational tasks. However, a hardware-based approach needs the identification of well-suited neuronal and synaptic models to obtain high functional and energy efficiency, which is a prime concern in size, weight, and power (SWaP) constrained environments. In this work, we perform a study on the characteristics of hardware neuron models (namely, inference errors, generalizability and robustness, practical implementability, and memory capacity) that have been proposed and demonstrated using a plethora of emerging nano-materials technology-based physical devices, to quantify the performance of such neurons on certain classes of problems that are of great importance in real-time signal processing like tasks in the context of reservoir computing. We find that the answer on which neuron to use for what applications depends on the particulars of the application requirements and constraints themselves, i.e., we need not only a hammer but all sorts of tools in our tool chest for high efficiency and quality neuromorphic computing.Comment: 13 pages, 6 figure

People's Perceptions About the Socio-Economic and Environmental Impact of Coastal Green Belt in Bangladesh

Understanding the perceptions and attitudes of local people towards afforestation is crucial for successful afforestation. To better understand the people's perceptions about the coastal green belt, we surveyed the Satkhira and Bhola districts during January 2021. A questionnaire survey of 200 respondents was conducted by a purposive and random sampling technique to obtain quantitative data. On the other hand, two Focus Group Discussions (FGDs) were performed to obtain qualitative data based on the diversity of age, sex, education, and occupation variables. We used to evaluate the respondent's perceptions about the coastal green belt by 5-point Likert scale data. The majority portion of the people in our study area was poor and their profession was fisherman and housewife among males and women respectively. The majority of them agreed that the green belt helped them in numerous ways, to rear their cattle and built a house near the green belt, by promoting the growth of crops, and further protect them from different natural calamities by reducing wind velocity. Shelterbelts greatly enhance tourism in that area, improved communication facilities, and increase their socio-economic condition and values. Shelterbelt has long-term benefits, peoples have positive attitudes towards shelterbelt and they seek training to maintain this shelterbelt smoothly. Respondents wanted fruit tree species as shelterbelt species near the embankment. This information can be used for policy formulation in terms of successful plantation by considering people's attitudes, which may work in both mitigation and adaptation of climate change in the coastal remote areas of Bangladesh