Training and development of Bengal Group of industries

This internship report is submitted in a partial fulfillment of the requirements for the degree of Bachelor of Business Administration, 2016.Cataloged from PDF version of Internship report.Includes bibliographical references (page 40).This report is about my internship at Bengal Group of Industries, Gulshan-1. In this report I have discussed every major aspect of my work experience during my internship period. In this report you will find information about the history of Bengal Group of industries, their businesses, organization timeline, organization structure etc. And in this report I also discussed about my work experience there and specific job responsibilities. Bengal Group of Industries has started their journey back in 1969 by establishing country's first plastic processing company- Bengal Plastics Ltd. Today, Bengal Group is one of the largest plastic industries in Bangladesh. However, their activities are not limited to plastics these days. Over the years they have successfully diversified our businesses into electronic media, real estate, chemical, paper, food, metal, and renewable energy. My report contains details about my job description at Bengal Group of Industries and detailed information about the industry I worked in along with a thorough analysis of a training program conducted by Bengal group of industries Human resource division. The main part of the report discusses about the Sales Training conducted by the Bengal Group of Industries and the analysis of that training session. Bengal Group of Industries is very focused on training and organization development. They always try to make their employees more effective and efficient. HR division of Bengal Group of industries has done the training and organization development tasks and this is a continuous process. The Bengal Group Industries training and development process is ideal process. It matched what I have learned in my courses. More or less, they are same. After working with them I found out that they actually care about their employee. The management always tries to provide their employee effective training in order to make them efficient and a better person. At the end it contains my major findings regarding the training program of Bengal Group of Industries.Navid Anjum KhanB. Business Administratio

Physics-inspired Ising Computing with Ring Oscillator Activated p-bits

The nearing end of Moore's Law has been driving the development of domain-specific hardware tailored to solve a special set of problems. Along these lines, probabilistic computing with inherently stochastic building blocks (p-bits) have shown significant promise, particularly in the context of hard optimization and statistical sampling problems. p-bits have been proposed and demonstrated in different hardware substrates ranging from small-scale stochastic magnetic tunnel junctions (sMTJs) in asynchronous architectures to large-scale CMOS in synchronous architectures. Here, we design and implement a truly asynchronous and medium-scale p-computer (with $\approx$ 800 p-bits) that closely emulates the asynchronous dynamics of sMTJs in Field Programmable Gate Arrays (FPGAs). Using hard instances of the planted Ising glass problem on the Chimera lattice, we evaluate the performance of the asynchronous architecture against an ideal, synchronous design that performs parallelized (chromatic) exact Gibbs sampling. We find that despite the lack of any careful synchronization, the asynchronous design achieves parallelism with comparable algorithmic scaling in the ideal, carefully tuned and parallelized synchronous design. Our results highlight the promise of massively scaled p-computers with millions of free-running p-bits made out of nanoscale building blocks such as stochastic magnetic tunnel junctions.Comment: To appear in the 22nd IEEE International Conference on Nanotechnology (IEEE-NANO 2022

High Electron Mobility Transistors: Performance Analysis, Research Trend and Applications

In recent years, high electron mobility transistors (HEMTs) have received extensive attention for their superior electron transport ensuring high speed and high power applications. HEMT devices are competing with and replacing traditional field‐effect transistors (FETs) with excellent performance at high frequency, improved power density and satisfactory efficiency. This chapter provides readers with an overview of the performance of some popular and mostly used HEMT devices. The chapter proceeds with different structures of HEMT followed by working principle with graphical illustrations. Device performance is discussed based on existing literature including both analytical and numerical models. Furthermore, some notable latest research works on HEMT devices have been brought into attention followed by prediction of future trends. Comprehensive knowledge of up‐to‐date results, future directions, and their analysis methodology would be helpful in designing novel HEMT devices

CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic inference and learning

Extending Moore's law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. Accelerating Monte Carlo algorithms that rely on random sampling with such CMOS+X technologies could have significant impact on a large number of fields from probabilistic machine learning, optimization to quantum simulation. In this paper, we show the combination of stochastic magnetic tunnel junction (sMTJ)-based probabilistic bits (p-bits) with versatile Field Programmable Gate Arrays (FPGA) to design a CMOS + X (X = sMTJ) prototype. Our approach enables high-quality true randomness that is essential for Monte Carlo based probabilistic sampling and learning. Our heterogeneous computer successfully performs probabilistic inference and asynchronous Boltzmann learning, despite device-to-device variations in sMTJs. A comprehensive comparison using a CMOS predictive process design kit (PDK) reveals that compact sMTJ-based p-bits replace 10,000 transistors while dissipating two orders of magnitude of less energy (2 fJ per random bit), compared to digital CMOS p-bits. Scaled and integrated versions of our CMOS + stochastic nanomagnet approach can significantly advance probabilistic computing and its applications in various domains by providing massively parallel and truly random numbers with extremely high throughput and energy-efficiency

A Survey of Weed Varieties in Samanabad, Lahore

A weed is an herbaceous plant that grows as a wild plant, and is considered a hindrance in the growth of preferred vegetation or cumbering the ground, and has no value for beauty or use. However, some weeds have roles in medicine, ecology and many other fields. A survey was conducted in Lahore to observe the weed varieties present in the area of Samanabad. The present study was carried out in May and June 2014.The primary purpose of the study was to gain knowledge about the availability of the total number of species present in this area. We also assessed whether these weeds were directly or indirectly beneficial for humans. Results of this study revealed a total of 33 species belonging to 20 different families which were collected and identified. Weeds were arranged in alphabetical order according to their respective families. Data inventory constitutes family name, botanical name, local name and life form. Results revealed the relative diversity of each family as Poaceae at 18.18% and Asteraceae at 15.15%. Out of 33 weed species, 64% were annual, 30% perennial and 6% biennial. The soil of the studied area was a hard, silty loam texture, with a slightly alkaline pH and low electrical conductivity. This study will be helpful in maintaining the flora of the Samanabad region

Massively Parallel Probabilistic Computing with Sparse Ising Machines

Inspired by the developments in quantum computing, building domain-specific classical hardware to solve computationally hard problems has received increasing attention. Here, by introducing systematic sparsification techniques, we demonstrate a massively parallel architecture: the sparse Ising Machine (sIM). Exploiting sparsity, sIM achieves ideal parallelism: its key figure of merit - flips per second - scales linearly with the number of probabilistic bits (p-bit) in the system. This makes sIM up to 6 orders of magnitude faster than a CPU implementing standard Gibbs sampling. Compared to optimized implementations in TPUs and GPUs, sIM delivers 5-18x speedup in sampling. In benchmark problems such as integer factorization, sIM can reliably factor semiprimes up to 32-bits, far larger than previous attempts from D-Wave and other probabilistic solvers. Strikingly, sIM beats competition-winning SAT solvers (by 4-700x in runtime to reach 95% accuracy) in solving 3SAT problems. Even when sampling is made inexact using faster clocks, sIM can find the correct ground state with further speedup. The problem encoding and sparsification techniques we introduce can be applied to other Ising Machines (classical and quantum) and the architecture we present can be used for scaling the demonstrated 5,000-10,000 p-bits to 1,000,000 or more through analog CMOS or nanodevices